Direct Mining of Discriminative Patterns for Classifying...
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Direct Mining of Discriminative Patterns for ClassifyingUncertain Data
Chuancong Gao, Jianyong Wang
Database LaboratoryDepartment of Computer Science and Technology
Tsinghua University, Beijing, China
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 1 / 26
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Classification on Certain Data
Example:
A toy example about certain categorical dataset containing 4 classes.Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / -Acceptable / - / /
Good - + / /Very Good / + + +
(+: Good, /: Medium, -: Bad)
A Lot of Methods:
I Decision Tree - C4.5, etc.
I Rule-based Classifier - Ripper, etc.
I Associative Classification (Pattern-based Classification) - CBA,RCBT, HARMONY, DDPMine, MbT, etc. (Better Performance)
I etc.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 2 / 26
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Classification on Certain Data
Example:
A toy example about certain categorical dataset containing 4 classes.Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / -Acceptable / - / /
Good - + / /Very Good / + + +
(+: Good, /: Medium, -: Bad)
A Lot of Methods:
I Decision Tree - C4.5, etc.
I Rule-based Classifier - Ripper, etc.
I Associative Classification (Pattern-based Classification) - CBA,RCBT, HARMONY, DDPMine, MbT, etc. (Better Performance)
I etc.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 2 / 26
![Page 4: Direct Mining of Discriminative Patterns for Classifying ...dbgroup.cs.tsinghua.edu.cn/chuancong/publications/kdd10...Direct Mining of Discriminative Patterns for Classifying Uncertain](https://reader036.fdocuments.us/reader036/viewer/2022071210/602121f598570b46701efff9/html5/thumbnails/4.jpg)
Classification on Certain Data
Example:
A toy example about certain categorical dataset containing 4 classes.Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / -Acceptable / - / /
Good - + / /Very Good / + + +
(+: Good, /: Medium, -: Bad)
A Lot of Methods:
I Decision Tree - C4.5, etc.
I Rule-based Classifier - Ripper, etc.
I Associative Classification (Pattern-based Classification) - CBA,RCBT, HARMONY, DDPMine, MbT, etc. (Better Performance)
I etc.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 2 / 26
![Page 5: Direct Mining of Discriminative Patterns for Classifying ...dbgroup.cs.tsinghua.edu.cn/chuancong/publications/kdd10...Direct Mining of Discriminative Patterns for Classifying Uncertain](https://reader036.fdocuments.us/reader036/viewer/2022071210/602121f598570b46701efff9/html5/thumbnails/5.jpg)
Classification on Certain Data
Example:
A toy example about certain categorical dataset containing 4 classes.Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / -Acceptable / - / /
Good - + / /Very Good / + + +
(+: Good, /: Medium, -: Bad)
A Lot of Methods:
I Decision Tree - C4.5, etc.
I Rule-based Classifier - Ripper, etc.
I Associative Classification (Pattern-based Classification) - CBA,RCBT, HARMONY, DDPMine, MbT, etc. (Better Performance)
I etc.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 2 / 26
![Page 6: Direct Mining of Discriminative Patterns for Classifying ...dbgroup.cs.tsinghua.edu.cn/chuancong/publications/kdd10...Direct Mining of Discriminative Patterns for Classifying Uncertain](https://reader036.fdocuments.us/reader036/viewer/2022071210/602121f598570b46701efff9/html5/thumbnails/6.jpg)
Classification on Certain Data
Example:
A toy example about certain categorical dataset containing 4 classes.Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / -Acceptable / - / /
Good - + / /Very Good / + + +
(+: Good, /: Medium, -: Bad)
A Lot of Methods:
I Decision Tree - C4.5, etc.
I Rule-based Classifier - Ripper, etc.
I Associative Classification (Pattern-based Classification) - CBA,RCBT, HARMONY, DDPMine, MbT, etc. (Better Performance)
I etc.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 2 / 26
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Associative Classification
Two-Step Framework:
I Mine a set of frequent patterns.
I Select a subset of most discriminative patterns from the minedpatterns.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 3 / 26
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Associative Classification
Two-Step Framework:
I Mine a set of frequent patterns.
I Select a subset of most discriminative patterns from the minedpatterns.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 3 / 26
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Associative Classification
Two-Step Framework:
I Mine a set of frequent patterns.
I Select a subset of most discriminative patterns from the minedpatterns.
One-Step Framework:
I Directly mine a set of most discriminative frequent patterns.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 3 / 26
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Associative Classification
Two-Step Framework:
I Mine a set of frequent patterns.
I Select a subset of most discriminative patterns from the minedpatterns.
One-Step Framework:
I Directly mine a set of most discriminative frequent patterns.
After Having Discriminative Patterns:
I Convert each pattern to a binary feature: Whether the instancecontains the pattern.
I Train a classifier using the feature data converted from training data.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 3 / 26
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Associative Classification
Two-Step Framework:
I Mine a set of frequent patterns.
I Select a subset of most discriminative patterns from the minedpatterns.
One-Step Framework:
I Directly mine a set of most discriminative frequent patterns.
After Having Discriminative Patterns:
I Convert each pattern to a binary feature: Whether the instancecontains the pattern.
I Train a classifier using the feature data converted from training data.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 3 / 26
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Associative Classification
Mainly Differences between Different Algorithms:
I Different types of mined pattern - All, Closed, Generator, etc.(Two-Step Framework)
I Different discriminative measures - Confidence (CBA, HARMONY),Information Gain (DDPMine) (Better Performance), Fisher Score, etc.
I Different instance covering strategies - Sequential Covering(DDPMine), Top-K (RCBT), Search Tree-based Partition (MbT), etc.
I Different classification models - Rule-based (RCBT, HARMONY),SVM (DDPMine, MbT) (Better Performance), Naıve Bayes, etc.
I Different feature types - Binary, Numeric (New, NDPMine)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 4 / 26
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Associative Classification
Mainly Differences between Different Algorithms:
I Different types of mined pattern - All, Closed, Generator, etc.(Two-Step Framework)
I Different discriminative measures - Confidence (CBA, HARMONY),Information Gain (DDPMine) (Better Performance), Fisher Score, etc.
I Different instance covering strategies - Sequential Covering(DDPMine), Top-K (RCBT), Search Tree-based Partition (MbT), etc.
I Different classification models - Rule-based (RCBT, HARMONY),SVM (DDPMine, MbT) (Better Performance), Naıve Bayes, etc.
I Different feature types - Binary, Numeric (New, NDPMine)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 4 / 26
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Associative Classification
Mainly Differences between Different Algorithms:
I Different types of mined pattern - All, Closed, Generator, etc.(Two-Step Framework)
I Different discriminative measures - Confidence (CBA, HARMONY),Information Gain (DDPMine) (Better Performance), Fisher Score, etc.
I Different instance covering strategies - Sequential Covering(DDPMine), Top-K (RCBT), Search Tree-based Partition (MbT), etc.
I Different classification models - Rule-based (RCBT, HARMONY),SVM (DDPMine, MbT) (Better Performance), Naıve Bayes, etc.
I Different feature types - Binary, Numeric (New, NDPMine)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 4 / 26
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Associative Classification
Mainly Differences between Different Algorithms:
I Different types of mined pattern - All, Closed, Generator, etc.(Two-Step Framework)
I Different discriminative measures - Confidence (CBA, HARMONY),Information Gain (DDPMine) (Better Performance), Fisher Score, etc.
I Different instance covering strategies - Sequential Covering(DDPMine), Top-K (RCBT), Search Tree-based Partition (MbT), etc.
I Different classification models - Rule-based (RCBT, HARMONY),SVM (DDPMine, MbT) (Better Performance), Naıve Bayes, etc.
I Different feature types - Binary, Numeric (New, NDPMine)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 4 / 26
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Associative Classification
Mainly Differences between Different Algorithms:
I Different types of mined pattern - All, Closed, Generator, etc.(Two-Step Framework)
I Different discriminative measures - Confidence (CBA, HARMONY),Information Gain (DDPMine) (Better Performance), Fisher Score, etc.
I Different instance covering strategies - Sequential Covering(DDPMine), Top-K (RCBT), Search Tree-based Partition (MbT), etc.
I Different classification models - Rule-based (RCBT, HARMONY),SVM (DDPMine, MbT) (Better Performance), Naıve Bayes, etc.
I Different feature types - Binary, Numeric (New, NDPMine)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 4 / 26
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Classification on Uncertain Data
Example:
A toy example about uncertain categorical dataset. The uncertaintyusually is caused by noise, measurement precisions, etc.
Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / {-: 0.8, /: 0.1, +: 0.1}Acceptable / - / {-: 0.1, /: 0.8, +: 0.1}
Good - + / {-: 0.1, /: 0.8, +: 0.1}Very Good / + + {-: 0.1, /: 0.1, +: 0.8}
(+: Good, /: Medium, -: Bad)
Very Few Methods:
I Uncertain Decision Tree - C4.5-based DTU.
I Uncertain Rule-based Classifier - Ripper-based uRule
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 5 / 26
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Classification on Uncertain Data
Example:
A toy example about uncertain categorical dataset. The uncertaintyusually is caused by noise, measurement precisions, etc.
Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / {-: 0.8, /: 0.1, +: 0.1}Acceptable / - / {-: 0.1, /: 0.8, +: 0.1}
Good - + / {-: 0.1, /: 0.8, +: 0.1}Very Good / + + {-: 0.1, /: 0.1, +: 0.8}
(+: Good, /: Medium, -: Bad)
Very Few Methods:
I Uncertain Decision Tree - C4.5-based DTU.
I Uncertain Rule-based Classifier - Ripper-based uRule
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 5 / 26
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Classification on Uncertain Data
Example:
A toy example about uncertain categorical dataset. The uncertaintyusually is caused by noise, measurement precisions, etc.
Evaluation Price Looking Tech. Spec. Quality
Unacceptable + - / {-: 0.8, /: 0.1, +: 0.1}Acceptable / - / {-: 0.1, /: 0.8, +: 0.1}
Good - + / {-: 0.1, /: 0.8, +: 0.1}Very Good / + + {-: 0.1, /: 0.1, +: 0.8}
(+: Good, /: Medium, -: Bad)
Very Few Methods:
I Uncertain Decision Tree - C4.5-based DTU.
I Uncertain Rule-based Classifier - Ripper-based uRule
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 5 / 26
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Our Solution
A new associative classification algorithm working on uncertain data.
Difference to Certain Dataset:Patterns involving uncertain attributes have probabilities to appear ininstances.
Challenges:
I How to represent frequentness information? - Using expected value ofsupport. (Easy to calculate - Sum all the probabilities appearing indifferent instances)
I How to represent discriminative information?
I How to cover instances?
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 6 / 26
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Our Solution
A new associative classification algorithm working on uncertain data.
Difference to Certain Dataset:Patterns involving uncertain attributes have probabilities to appear ininstances.
Challenges:
I How to represent frequentness information? - Using expected value ofsupport. (Easy to calculate - Sum all the probabilities appearing indifferent instances)
I How to represent discriminative information?
I How to cover instances?
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 6 / 26
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Our Solution
A new associative classification algorithm working on uncertain data.
Difference to Certain Dataset:Patterns involving uncertain attributes have probabilities to appear ininstances.
Challenges:
I How to represent frequentness information? - Using expected value ofsupport. (Easy to calculate - Sum all the probabilities appearing indifferent instances)
I How to represent discriminative information?
I How to cover instances?
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 6 / 26
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Our Solution
A new associative classification algorithm working on uncertain data.
Difference to Certain Dataset:Patterns involving uncertain attributes have probabilities to appear ininstances.
Challenges:
I How to represent frequentness information? - Using expected value ofsupport. (Easy to calculate - Sum all the probabilities appearing indifferent instances)
I How to represent discriminative information?
I How to cover instances?
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 6 / 26
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Our Solution
A new associative classification algorithm working on uncertain data.
Difference to Certain Dataset:Patterns involving uncertain attributes have probabilities to appear ininstances.
Challenges:
I How to represent frequentness information? - Using expected value ofsupport. (Easy to calculate - Sum all the probabilities appearing indifferent instances)
I How to represent discriminative information?
I How to cover instances?
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 6 / 26
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Discriminative Measures on Uncertain Data
Choose to use expected value of confidence. Unlike expected support,expected confidence is hard to calculate.
Definition of Expected Confidence:
Given a set of transactions T and the set of possible worlds W w.r.t. T ,the expected confidence of an itemset x on class c is
E (confxc) =
∑wi∈W
confx ,wic × P(wi ) =
∑wi∈W
supx ,wic
supx ,wi
× P(wi )
where P(wi ) is the probability of world wi . confx ,wic is the respected
confidence of x on class c in world wi , while supx ,wi (supx ,wic) is the
respected support of x (on class c) in world wi .
O(∏
Ak∈Au |domAk||T |) possible worlds.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 7 / 26
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Discriminative Measures on Uncertain Data
Choose to use expected value of confidence. Unlike expected support,expected confidence is hard to calculate.
Definition of Expected Confidence:
Given a set of transactions T and the set of possible worlds W w.r.t. T ,the expected confidence of an itemset x on class c is
E (confxc) =
∑wi∈W
confx ,wic × P(wi ) =
∑wi∈W
supx ,wic
supx ,wi
× P(wi )
where P(wi ) is the probability of world wi . confx ,wic is the respected
confidence of x on class c in world wi , while supx ,wi (supx ,wic) is the
respected support of x (on class c) in world wi .
O(∏
Ak∈Au |domAk||T |) possible worlds.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 7 / 26
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Discriminative Measures on Uncertain Data
Choose to use expected value of confidence. Unlike expected support,expected confidence is hard to calculate.
Definition of Expected Confidence:
Given a set of transactions T and the set of possible worlds W w.r.t. T ,the expected confidence of an itemset x on class c is
E (confxc) =
∑wi∈W
confx ,wic × P(wi ) =
∑wi∈W
supx ,wic
supx ,wi
× P(wi )
where P(wi ) is the probability of world wi . confx ,wic is the respected
confidence of x on class c in world wi , while supx ,wi (supx ,wic) is the
respected support of x (on class c) in world wi .
O(∏
Ak∈Au |domAk||T |) possible worlds.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 7 / 26
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Efficient Computation of Expected Confidence
Lemma:Since 0 ≤ supx
c ≤ supx ≤ |T |, we have:
E (confxc) =
∑wi∈W
confx ,wic × P(wi )
=
|T |∑i=0
i∑j=0
j
i× P(supx = i ∧ supx
c = j)
=
|T |∑i=0
Ei (supxc)
i=
|T |∑i=0
Ei (confxc)
, where Ei (supxc) and Ei (confx
c) denote the part of expected support andconfidence of itemset x on class c when supx = i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 8 / 26
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Efficient Computation of Expected Confidence
Given 0 ≤ n ≤ |T |, define En(supxc) =
∑|T |i=0 Ei ,n(supx
c) as the expectedsupport of x on class c on the first n transactions of T , and Ei ,n(supx
c) asthe expected support of x on class c with support of i on the first ntransactions of T .
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c)
+ (1− pn)× Ei ,n−1(supxc)
when cn 6= c , and
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c + 1)
+ (1− pn)× Ei ,n−1(supxc)
when cn = c, where 1 ≤ i ≤ n ≤ |T |.
Ei ,n(supxc) = 0
for ∀n where i = 0, or where n < i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 9 / 26
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Efficient Computation of Expected Confidence
Given 0 ≤ n ≤ |T |, define En(supxc) =
∑|T |i=0 Ei ,n(supx
c) as the expectedsupport of x on class c on the first n transactions of T , and Ei ,n(supx
c) asthe expected support of x on class c with support of i on the first ntransactions of T .
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c)
+ (1− pn)× Ei ,n−1(supxc)
when cn 6= c
, and
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c + 1)
+ (1− pn)× Ei ,n−1(supxc)
when cn = c, where 1 ≤ i ≤ n ≤ |T |.
Ei ,n(supxc) = 0
for ∀n where i = 0, or where n < i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 9 / 26
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Efficient Computation of Expected Confidence
Given 0 ≤ n ≤ |T |, define En(supxc) =
∑|T |i=0 Ei ,n(supx
c) as the expectedsupport of x on class c on the first n transactions of T , and Ei ,n(supx
c) asthe expected support of x on class c with support of i on the first ntransactions of T .
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c)
+ (1− pn)× Ei ,n−1(supxc)
when cn 6= c , and
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c + 1)
+ (1− pn)× Ei ,n−1(supxc)
when cn = c, where 1 ≤ i ≤ n ≤ |T |.
Ei ,n(supxc) = 0
for ∀n where i = 0, or where n < i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 9 / 26
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Efficient Computation of Expected Confidence
Given 0 ≤ n ≤ |T |, define En(supxc) =
∑|T |i=0 Ei ,n(supx
c) as the expectedsupport of x on class c on the first n transactions of T , and Ei ,n(supx
c) asthe expected support of x on class c with support of i on the first ntransactions of T .
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c)
+ (1− pn)× Ei ,n−1(supxc)
when cn 6= c , and
Ei ,n(supxc) = pn × Ei−1,n−1(supx
c + 1)
+ (1− pn)× Ei ,n−1(supxc)
when cn = c, where 1 ≤ i ≤ n ≤ |T |.
Ei ,n(supxc) = 0
for ∀n where i = 0, or where n < i .C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 9 / 26
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Efficient Computation of Expected Confidence
Defining Pi ,n as the probability of x having support of i on the first ntransactions of T , we have
Ei ,n(supxc) = pn × (Ei−1,n−1(supx
c + 1))
+ (1− pn)× Ei ,n−1(supxc)
= pn × (Ei−1,n−1(supxc) + Pi−1,n−1)
+ (1− pn)× Ei ,n−1(supxc)
when cn = c
, since we have:
Ei−1,n−1(supxc + 1) = Ei−1,n−1(supx
c) + Pi−1,n−1
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 10 / 26
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Efficient Computation of Expected Confidence
Defining Pi ,n as the probability of x having support of i on the first ntransactions of T , we have
Ei ,n(supxc) = pn × (Ei−1,n−1(supx
c + 1))
+ (1− pn)× Ei ,n−1(supxc)
= pn × (Ei−1,n−1(supxc) + Pi−1,n−1)
+ (1− pn)× Ei ,n−1(supxc)
when cn = c , since we have:
Ei−1,n−1(supxc + 1) = Ei−1,n−1(supx
c) + Pi−1,n−1
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 10 / 26
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Efficient Computation of Expected Confidence
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Pi ,n = pn × Pi−1,n−1 + (1− pn)× Pi ,n−1
, where 1 ≤ i ≤ n ≤ |T |.
Pi ,n =
{1 for n = 0
Pi ,n−1 × (1− pn) for 1 ≤ n ≤ |T |
where i = 0.Pi ,n = 0
where n < i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 11 / 26
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Efficient Computation of Expected Confidence
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Pi ,n = pn × Pi−1,n−1 + (1− pn)× Pi ,n−1
, where 1 ≤ i ≤ n ≤ |T |.
Pi ,n =
{1 for n = 0
Pi ,n−1 × (1− pn) for 1 ≤ n ≤ |T |
where i = 0.
Pi ,n = 0
where n < i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 11 / 26
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Efficient Computation of Expected Confidence
Denoting P(x ⊆ ti ) as pi for each transaction ti ∈ T , we have
Pi ,n = pn × Pi−1,n−1 + (1− pn)× Pi ,n−1
, where 1 ≤ i ≤ n ≤ |T |.
Pi ,n =
{1 for n = 0
Pi ,n−1 × (1− pn) for 1 ≤ n ≤ |T |
where i = 0.Pi ,n = 0
where n < i .
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 11 / 26
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Efficient Computation of Expected Confidence
Since E (confxc) = E|T |(confx
c) =∑|T |
i=0 Ei ,|T |(confxc). The computation
is divided into |T |+ 1 steps with Ei ,|T |(confxc) = Ei ,|T |(supx
c)/i(0 ≤ i ≤ |T |) computed in ith step.
#Transaction / n
Support / i
0 1 |T|
01
|T|
...
...
,| |( )ci T xconfE
1,| | 1( )ci T xconfE − −
2
2
Computation in One Step Start of Next Step Explaination
1,| | ( )ci T xconfE −
| |
,| |0
( ) ( )T
c cx i T x
i
conf cE E onf=
=∑ Time Complexity:
O(|T |2)
Space Complexity:
O(|T |)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 12 / 26
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Efficient Computation of Expected Confidence
Since E (confxc) = E|T |(confx
c) =∑|T |
i=0 Ei ,|T |(confxc). The computation
is divided into |T |+ 1 steps with Ei ,|T |(confxc) = Ei ,|T |(supx
c)/i(0 ≤ i ≤ |T |) computed in ith step.
#Transaction / n
Support / i
0 1 |T|
01
|T|
...
...
,| |( )ci T xconfE
1,| | 1( )ci T xconfE − −
2
2
Computation in One Step Start of Next Step Explaination
1,| | ( )ci T xconfE −
| |
,| |0
( ) ( )T
c cx i T x
i
conf cE E onf=
=∑
Time Complexity:
O(|T |2)
Space Complexity:
O(|T |)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 12 / 26
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Efficient Computation of Expected Confidence
Since E (confxc) = E|T |(confx
c) =∑|T |
i=0 Ei ,|T |(confxc). The computation
is divided into |T |+ 1 steps with Ei ,|T |(confxc) = Ei ,|T |(supx
c)/i(0 ≤ i ≤ |T |) computed in ith step.
#Transaction / n
Support / i
0 1 |T|
01
|T|
...
...
,| |( )ci T xconfE
1,| | 1( )ci T xconfE − −
2
2
Computation in One Step Start of Next Step Explaination
1,| | ( )ci T xconfE −
| |
,| |0
( ) ( )T
c cx i T x
i
conf cE E onf=
=∑ Time Complexity:
O(|T |2)
Space Complexity:
O(|T |)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 12 / 26
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Efficient Computation of Expected Confidence
Since E (confxc) = E|T |(confx
c) =∑|T |
i=0 Ei ,|T |(confxc). The computation
is divided into |T |+ 1 steps with Ei ,|T |(confxc) = Ei ,|T |(supx
c)/i(0 ≤ i ≤ |T |) computed in ith step.
#Transaction / n
Support / i
0 1 |T|
01
|T|
...
...
,| |( )ci T xconfE
1,| | 1( )ci T xconfE − −
2
2
Computation in One Step Start of Next Step Explaination
1,| | ( )ci T xconfE −
| |
,| |0
( ) ( )T
c cx i T x
i
conf cE E onf=
=∑ Time Complexity:
O(|T |2)
Space Complexity:
O(|T |)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 12 / 26
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Upper Bounds of Expected Confidence
For ∀i(1 ≤ i ≤ |T |), we have
E (confxc) = E|T |(confx
c)
=i−1∑k=0
Ek,|T |(supxc)
k+
|T |∑k=i
Ek,|T |(supxc)
k
≤i−1∑k=0
Ek,|T |(supxc)
k+
|T |∑k=i
Ek,|T |(supxc)
i
=i−1∑k=0
Ek,|T |(supxc)
k+
|T |∑k=0
Ek,|T |(supxc)
i−
i−1∑k=0
Ek,|T |(supxc)
i
=i−1∑k=0
Ek,|T |(supxc)× (
1
k− 1
i) +
E (supxc)
i
=boundi (confxc)
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 13 / 26
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Upper Bounds of Expected Confidence
For 1 ≤ i ≤ |T |, we have:
E (supxc) = bound1(confx
c)
≥ · · · ≥ boundi (confxc) ≥ · · ·
≥ bound|T |(confxc) = E (confx
c)
Since
boundi (confxc) = boundi−1(confx
c)
− (1
i − 1− 1
i)× (E (supx
c)−i−1∑k=0
Ek,|T |(supxc))
, can compute boundi (confxc) with boundi−1(confx
c).
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 14 / 26
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Upper Bounds of Expected Confidence
For 1 ≤ i ≤ |T |, we have:
E (supxc) = bound1(confx
c)
≥ · · · ≥ boundi (confxc) ≥ · · ·
≥ bound|T |(confxc) = E (confx
c)
Since
boundi (confxc) = boundi−1(confx
c)
− (1
i − 1− 1
i)× (E (supx
c)−i−1∑k=0
Ek,|T |(supxc))
, can compute boundi (confxc) with boundi−1(confx
c).
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 14 / 26
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Upper Bounds of Expected Confidence
#Transaction / n
Support / i
0 1 |T|
01
|T|
...
...
,| |( )ci T xconfE
1,| |( )ci T xconfE −
1,| | 1( )ci T xconfE − −
2
2
Stop Condition:
SkippedComputation in One Step Start of Next Step Explaination
_( )cc cur db
i x maxbound conf conf≤
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 15 / 26
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Upper Bounds of Expected Confidence
Running Example:
0.1
1
10
10 20 30 40 50 60 70 80 90 100
boun
d i(c
onf x
c )
Support / i
boundi(confxc)
confmaxcur_dbc
confxc
boundi(confxc) (Skipped)
Stop when boundi(confxc) <= confmax
cur_dbc
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 16 / 26
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Algorithm Framework
1. Calculate the (expected) confidence of current prefix pattern.
2. If the confidence value is larger than previous maximal value, updatecovered instances.
3. If at least one instance covered, select the prefix.
4. Continue growing current prefix, and go to Step 1.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 17 / 26
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Algorithm Framework
1. Calculate the (expected) confidence of current prefix pattern.
2. If the confidence value is larger than previous maximal value, updatecovered instances.
3. If at least one instance covered, select the prefix.
4. Continue growing current prefix, and go to Step 1.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 17 / 26
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Algorithm Framework
1. Calculate the (expected) confidence of current prefix pattern.
2. If the confidence value is larger than previous maximal value, updatecovered instances.
3. If at least one instance covered, select the prefix.
4. Continue growing current prefix, and go to Step 1.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 17 / 26
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Algorithm Framework
1. Calculate the (expected) confidence of current prefix pattern.
2. If the confidence value is larger than previous maximal value, updatecovered instances.
3. If at least one instance covered, select the prefix.
4. Continue growing current prefix, and go to Step 1.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 17 / 26
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Algorithm Framework
1. Calculate the (expected) confidence of current prefix pattern.
2. If the confidence value is larger than previous maximal value, updatecovered instances.
3. If at least one instance covered, select the prefix.
4. Continue growing current prefix, and go to Step 1.
Need to sort all the uncertain attributes after certain attributes, to helpshrink current projected database.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 17 / 26
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Instance Covering Strategy
Previous Strategy in HARMONY:
Just find one most discriminative covering pattern with the highestconfidence for each instance. On uncertain data, the probability of theinstance being covered could be very low.
Our method:Apply a threshold of minimum cover probability coverProbmin. Assure thatthe probability of each instance not covered by any pattern is less than1− coverProbmin, by maintaining a list storing confidence values ofcovering patterns on class c in descending order.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 18 / 26
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Instance Covering Strategy
Previous Strategy in HARMONY:
Just find one most discriminative covering pattern with the highestconfidence for each instance. On uncertain data, the probability of theinstance being covered could be very low.
Our method:Apply a threshold of minimum cover probability coverProbmin. Assure thatthe probability of each instance not covered by any pattern is less than1− coverProbmin, by maintaining a list storing confidence values ofcovering patterns on class c in descending order.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 18 / 26
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Used Classifiers
SVM ClassifierConvert each pattern to a binary feature by whether it is contained by theinstance.
Rule-based Classifier (From HARMONY)
For each test instance we just sum up the product of the confidence ofeach pattern on each class and the probability of the instance containingthe pattern. The class with the largest value is the predicted class of theinstance.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 19 / 26
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Used Classifiers
SVM ClassifierConvert each pattern to a binary feature by whether it is contained by theinstance.
Rule-based Classifier (From HARMONY)
For each test instance we just sum up the product of the confidence ofeach pattern on each class and the probability of the instance containingthe pattern. The class with the largest value is the predicted class of theinstance.
C. Gao, J. Wang (Tsinghua Univ.) Direct Mining of Discriminative Patterns for Classifying Uncertain Data 19 / 26
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Used DatasetsDataset #Instance #Attribute #Class Area
australian 690 14 2 Financialbalance 635 4 3 Socialbands 539 38 2 Physicalbreast 699 9 2 Life
bridges-v1 106 11 6 N/Abridges-v2 106 10 6 N/A
car 1728 6 4 N/Acontraceptive 1473 9 3 Life
credit 690 15 2 Financialechocardiogram 131 12 2 Life
flag 194 28 8 N/Agerman 1000 19 2 Financial
heart 920 13 5 Lifehepatitis 155 19 2 Life
horse 368 27 2 Lifemonks-1 556 6 2 N/Amonks-2 601 6 2 N/Amonks-3 554 6 2 N/A
mushroom 8124 22 2 Lifepima 768 8 2 Life
postoperative 90 8 3 Lifepromoters 106 57 2 Life
spect 267 22 2 Lifesurvival 306 3 2 Lifeta eval 151 5 3 N/A
tic-tac-toe 958 9 2 Gamevehicle 846 18 4 N/Avoting 435 16 2 Socialwine 178 13 3 Physicalzoo 101 16 7 Life
30 Public UCI CertainDatasets
Real values have beendiscretizated.
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Used DatasetsDataset #Instance #Attribute #Class Area
australian 690 14 2 Financialbalance 635 4 3 Socialbands 539 38 2 Physicalbreast 699 9 2 Life
bridges-v1 106 11 6 N/Abridges-v2 106 10 6 N/A
car 1728 6 4 N/Acontraceptive 1473 9 3 Life
credit 690 15 2 Financialechocardiogram 131 12 2 Life
flag 194 28 8 N/Agerman 1000 19 2 Financial
heart 920 13 5 Lifehepatitis 155 19 2 Life
horse 368 27 2 Lifemonks-1 556 6 2 N/Amonks-2 601 6 2 N/Amonks-3 554 6 2 N/A
mushroom 8124 22 2 Lifepima 768 8 2 Life
postoperative 90 8 3 Lifepromoters 106 57 2 Life
spect 267 22 2 Lifesurvival 306 3 2 Lifeta eval 151 5 3 N/A
tic-tac-toe 958 9 2 Gamevehicle 846 18 4 N/Avoting 435 16 2 Socialwine 178 13 3 Physicalzoo 101 16 7 Life
30 Public UCI CertainDatasets
Real values have beendiscretizated.
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Convert to Uncertain Datasets
Two parameters:
Uncertain Attribute Number:Number of attributes selected to converted to uncertain. Those withhighest information gain values are selected.
Uncertain Degree (0 - 1):
The probability the attribute value taking values other than the originalvalue.
Represented by Ux@y, where x is uncertain degree and y is uncertainattribute number.
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Convert to Uncertain Datasets
Two parameters:
Uncertain Attribute Number:Number of attributes selected to converted to uncertain. Those withhighest information gain values are selected.
Uncertain Degree (0 - 1):
The probability the attribute value taking values other than the originalvalue.
Represented by Ux@y, where x is uncertain degree and y is uncertainattribute number.
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Convert to Uncertain Datasets
Two parameters:
Uncertain Attribute Number:Number of attributes selected to converted to uncertain. Those withhighest information gain values are selected.
Uncertain Degree (0 - 1):
The probability the attribute value taking values other than the originalvalue.
Represented by Ux@y, where x is uncertain degree and y is uncertainattribute number.
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Accuracy Evaluation
Average accuracies on 30 datasets. For accuracy on each dataset, refer toour paper.
Using SVM Classifier:Dataset uHARMONY DTU uRule
U10@1 79.0138 74.8738 75.2111U10@2 78.6970 73.1629 73.4107U10@4 77.9657 72.2670 69.4649
U20@1 78.9537 74.6577 74.6287U20@2 78.6073 72.5642 72.5460U20@4 77.8352 69.9157 68.2066
Using Rule-based Classifier:Dataset uHARMONYrule DTU uRule
U10@4 73.2517 72.2670 69.4649
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Accuracy Evaluation
Average accuracies on 30 datasets. For accuracy on each dataset, refer toour paper.
Using SVM Classifier:Dataset uHARMONY DTU uRule
U10@1 79.0138 74.8738 75.2111U10@2 78.6970 73.1629 73.4107U10@4 77.9657 72.2670 69.4649
U20@1 78.9537 74.6577 74.6287U20@2 78.6073 72.5642 72.5460U20@4 77.8352 69.9157 68.2066
Using Rule-based Classifier:Dataset uHARMONYrule DTU uRule
U10@4 73.2517 72.2670 69.4649
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Sensitivity Test
94
94.5
95
95.5
96
96.5
97
1.25 2.5 5 10 20
Acc
urac
y (in
%)
Minimum Support (in %)
(a) breast
49
50
51
52
53
54
0.25 0.5 1 2 4
Acc
urac
y (in
%)
Minimum Support (in %)
(b) wine
Figure: Accuracy Evaluation of U10@1 w.r.t. Minimum Support
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Sensitivity Test
94
94.5
95
95.5
96
96.5
0 10 50 90 100
Acc
urac
y (in
%)
Minimum Cover Prob. (in %)
(a) breast
51
51.5
52
52.5
53
53.5
0 10 50 90 100
Acc
urac
y (in
%)
Minimum Cover Prob. (in %)
(b) wine
Figure: Accuracy Evaluation of U10@1 w.r.t. Minimum Cover Prob.
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Runtime Efficiency
uHarmony DTU uRule
0.1
1
10
100
1000
breast car contraceptive heart pima wine
Run
ning
Tim
e (in
sec
)
Dataset
(a) Running Time (in sec)
Figure: Classification Efficiency Evaluation of U10@1
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Runtime Efficiency
uHarmony DTU uRule
10
100
1000
breast car contraceptive heart pima wine
Mem
ory
Use
(in
MB
)
Dataset
(b) Memory Use (in MB)
Figure: Classification Efficiency Evaluation of U10@1
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Effectiveness of the Expected Confidence Upper Bound
With Expected Conf Bound Without Expected Conf Bound
10 15 20 25 30 35 40 45 50
0.2 0.4 0.6 0.8 1 1.2
Run
ning
Tim
e (in
sec
)
Minimum Support (in %)
(a) car
10
15
20
25
30
35
40
0.2 0.4 0.6 0.8 1 1.2
Run
ning
Tim
e (in
sec
)
Minimum Support (in %)
(b) heart
Figure: Running Time Evaluation of U10@4
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Scalability Test
With Expected Conf Bound Without Expected Conf Bound
0 20 40 60 80
100 120 140 160 180
1 2 4 8 16
Run
ning
Tim
e (in
sec
)
Dataset Duplication Ratio
(a) car (supmin = 0.01)
0
200
400
600
800
1000
1 2 4 8 16
Run
ning
Tim
e (in
sec
)
Dataset Duplication Ratio
(b) heart (supmin = 0.01)
Figure: Scalability Evaluation (U10@1, Running Time)
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Conclusions
I Proposed the first associative classification algorithm on uncertaindata.
I Proposed an efficient computation of expected confidence value,together with the computation of upper bounds.
I New instance covering strategy has been proposed and tested to beeffective.
I Conducted an extensive evaluation on 30 public real data, undervarying uncertain parameters. With significant improvements onaccuracy, comparing with two other state-of-the-art alrotihms.
I Evaluated the runtime efficiency, proved the effectiveness of usingupper bounds.
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Conclusions
I Proposed the first associative classification algorithm on uncertaindata.
I Proposed an efficient computation of expected confidence value,together with the computation of upper bounds.
I New instance covering strategy has been proposed and tested to beeffective.
I Conducted an extensive evaluation on 30 public real data, undervarying uncertain parameters. With significant improvements onaccuracy, comparing with two other state-of-the-art alrotihms.
I Evaluated the runtime efficiency, proved the effectiveness of usingupper bounds.
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Conclusions
I Proposed the first associative classification algorithm on uncertaindata.
I Proposed an efficient computation of expected confidence value,together with the computation of upper bounds.
I New instance covering strategy has been proposed and tested to beeffective.
I Conducted an extensive evaluation on 30 public real data, undervarying uncertain parameters. With significant improvements onaccuracy, comparing with two other state-of-the-art alrotihms.
I Evaluated the runtime efficiency, proved the effectiveness of usingupper bounds.
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Conclusions
I Proposed the first associative classification algorithm on uncertaindata.
I Proposed an efficient computation of expected confidence value,together with the computation of upper bounds.
I New instance covering strategy has been proposed and tested to beeffective.
I Conducted an extensive evaluation on 30 public real data, undervarying uncertain parameters. With significant improvements onaccuracy, comparing with two other state-of-the-art alrotihms.
I Evaluated the runtime efficiency, proved the effectiveness of usingupper bounds.
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Conclusions
I Proposed the first associative classification algorithm on uncertaindata.
I Proposed an efficient computation of expected confidence value,together with the computation of upper bounds.
I New instance covering strategy has been proposed and tested to beeffective.
I Conducted an extensive evaluation on 30 public real data, undervarying uncertain parameters. With significant improvements onaccuracy, comparing with two other state-of-the-art alrotihms.
I Evaluated the runtime efficiency, proved the effectiveness of usingupper bounds.
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The End
Thank you for Listening!
Questions or Comments?
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