TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents
25
TagLearner: A P2P Classifier Learning TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Tex System from Collaboratively Tagged Tex t Documents t Documents Haimonti Dutta Haimonti Dutta 1 , Xianshu Zhu , Xianshu Zhu 2 , Tushar Muhale , Tushar Muhale 2 , Hillol Kargupta , Hillol Kargupta 2 , , Kirk Borne Kirk Borne 3 , Codrina Lauth , Codrina Lauth 4 , Florian Holz , Florian Holz 5 , and Gerherd Heyer , and Gerherd Heyer 5 1 Columbia University 2 University of Maryland, Baltimore County 3 George Mason University 4 Fraunhofer Institute for Intelligent Analysis and Information Systems 5 University of Leipzig
description
TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents. Haimonti Dutta 1 , Xianshu Zhu 2 , Tushar Muhale 2 , Hillol Kargupta 2 , Kirk Borne 3 , Codrina Lauth 4 , Florian Holz 5 , and Gerherd Heyer 5. 1 Columbia University - PowerPoint PPT Presentation
Transcript of TagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documents
TagLearner: A P2P Classifier Learning System frTagLearner: A P2P Classifier Learning System from Collaboratively Tagged Text Documentsom Collaboratively Tagged Text Documents
Haimonti DuttaHaimonti Dutta11, Xianshu Zhu, Xianshu Zhu22, Tushar Muhale, Tushar Muhale22, Hillol Kargupta, Hillol Kargupta22, Kirk Borne, Kirk Borne33, , Codrina LauthCodrina Lauth44, Florian Holz, Florian Holz55, and Gerherd Heyer, and Gerherd Heyer55
1Columbia University 2University of Maryland, Baltimore County
3George Mason University4Fraunhofer Institute for Intelligent Analysis and Information Systems
5University of Leipzig
OutlineOutline• Introduction and Motivation• Related Work• TagLearner• Distributed Classifier-learning Algorithm• Experiments• Conclusion and Future Work
IntroductionIntroduction• Large Online Document Repositories:
– Online Newspapers, Digital Libraries, etc.– Growing in size
• Text categorization on the repositories:– No automated text classification mechanism– Performed by authorities, such as librarians
Impractical
Introduction (cont.)Introduction (cont.)• Collaborative tagging
– Del.icio.us, Flickr, Google image labeler– Recruit web users to add tags to a resource– Help to utilize power of people’s knowledge
• Pros and cons– Improve web search result, help on classification– Not support by most online text repositories – Lack of control
• Absence of standard keywords• Errors in tagging due to spelling errors• Harder to manage due to increased content diversity
MotivationMotivation• Provide automated classification service
– Utilize collaborative effort of users • Collaborative tagging in Peer-to-Peer network
– Without repositories’ support
P2P Classifier learning system
Related WorkRelated Work• Collaborative tagging:
– Recommendation System (Tso-Sutter et al.)– Web search (Yahia et al.)– Classification accuracy (Brooks et al.)
• Distributed Linear Programming:– Distributed Simplex Algorithm (Dutta et al.)
TagLearner: A P2P Classifier Learning SystemTagLearner: A P2P Classifier Learning System
TagLearner: A P2P Classifier Learning SystemTagLearner: A P2P Classifier Learning System
TagLearner: A P2P Classifier Learning SystemTagLearner: A P2P Classifier Learning System
TagLearner: A P2P Classifier Learning SystemTagLearner: A P2P Classifier Learning System
Service provider:
provide P2P classifier learning service
TagLearnerTagLearner
- Register service by creating a tagging group
- Maintain a tagging group for this service- Predefined Labels used for tagging- Features for classification- Group members- Learnt classifier model
TagLearnerTagLearner
• Interface: - Join or leave the tagging group - Tag the web documents• Distributed classifier learning algorithm
Client side browser plugin
Classifier Design by Linear Classifier Design by Linear ProgrammingProgramming
• Classification problem can be framed as a linear programming problem
kx
Class 1
Class 2
:feature vector of k-th instanceW : weight vector We want to find a W such that:
W can be found by minimizing the error
dWxk
kx
Classifier Design by Linear Classifier Design by Linear ProgrammingProgramming
• Maximize: Subject to: where
Use Simplex Method to solve it!
EWz TT SDEW
Px
x
x
...2
1
T
PeeeE ),...,,( 21 TdddD ),...,,( ),...,( 21 PsssS ),...,,( 21 P
Distributed Linear ProgrammingDistributed Linear Programming• Distributed data
– Each user only has a collection of constraints
• Objective function: • Constraints:
05.21167 321 wwwz
5.072 321 www5.033 321 www
5.03 321 www5.05.672 321 www
5.024 321 www
Z W1 W2 W3 value
1 -7 -16 -21.5 0
0 2 1 7 0.5
0 1 3 3 0.5
0 1 4 2 0.5
0 1 1 3 0.5
0 2 7 6.5 0.5
Simplex Tableau
Distributed Simplex AlgorithmDistributed Simplex Algorithm
Each user has different constraints, but wants to solve
the same objective function.
User A User B
User C
User D
Distributed Simplex AlgorithmDistributed Simplex Algorithm
User A User B
User C
User D
Distributed Simplex AlgorithmDistributed Simplex Algorithm
0.5/7=1/14
0.5/3=1/6
0.5/2=1/4
0.5/3=1/6
0.5/6.5=13/4
User A User B
User C
User D
Distributed Simplex AlgorithmDistributed Simplex Algorithm
0.5/7=1/14
0.5/3=1/6
0.5/2=1/4
0.5/3=1/6
0.5/6.5=13/4
User A User B
User C
User D
Experimental ResultsExperimental Results• Distributed Data Mining Toolkit (DDMT)
• “NSF Research Awards Abstracts 1990-2003” data set from the UCI Machine Learning Repository
• We only consider abstracts belonging to Earth and Mathematical sciences
• Features used for classification do not rely on collaboratively generated annotations.
Experiments (cont.)Experiments (cont.)
Figure 1. Communication cost versus the number of nodes in the network
Experiments (cont.)Experiments (cont.)
Conclusion and Future WorkConclusion and Future Work• Conclusion:
– P2P classifier learning system prototype – Scalable distributed classification algorithm based on
linear programming
• Future work:– extension of the classification algorithm for multi-class
classification problems – Improve classification accuracy
Thank you !Thank you !
Questions ?Questions ?
