ICET Presentation 1

7/31/2019 ICET Presentation 1

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A Feature Subset Selection Methodbased on Ant Colony Optimization

and Symmetric Uncertainty

Authors: Syed Imran Ali*,Dr. Wasem Shahzad. (NU-FAST)

* Presenter


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Presentation Layout

Introduction

Motivation

Background on Feature Selection

Proposed Technique

Ant Colony Optimization

Symmetric Uncertainty Experimentation

Conclusion

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Motivation

Why Feature Selection?

Curse of Dimensionality

Three-fold benefits

Enhance the capabilities of Filter based methods

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Data Reduction

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Feature Selection Types

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PROPOSED TECHNIQUE


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Proposed Technique

Basic Ingredients of ACO Graph Representation

Heuristic Desirability

Positive feedback process

Constraint Satisfaction Solution Construction mechanism

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Basic Ingredients of ACO

Graph Representation

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Heuristic desirability and PositiveFeedback mechanism


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Constraint Satisfaction and SolutionConstruction


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Information Theoretic Measure

Information Gain

Symmetric Uncertainty

IG (Y,X) = H(Y) H (Y|X)

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ACO-SU

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EXPERIMENTATION


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Experimentation Framework

All the experiments are performed using 10-FoldCross Validation and results of ten runs are

averaged.

Proposed method is compared with four otherfeature selection algorithms.

Performance Metrics:

Number of Features Selected.

Predictive Classification Accuracy using 10-FCV.

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SNO. Dataset Total Features Instances Classes1 * Iris 4 150 32 Liver Disorder 6 345 23 Diabetes 8 768 24 Breast Cancer- W 9 699 25 Vote 16 435 26 Labor 16 57 27 Hepatitis 19 155 28 Colic-Horse 22 368 29 Ionosphere 34 351 210 Lymph 18 148 411 Dermatology 34 366 612 Lung Cancer 56 32 313 Audiology 69 226 24

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Parameters Values

Number of Ants 20

Alpha 1

Beta 1

Evaporation Rate 0.15

Max. Epochs 500

Path Convergence Threshold 50

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Experiment

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Experiment

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Experiment

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Features Selected by ACO-SU

Dataset Total ACO-SUIris 4 2Liver Disorder 6 2Diabetes 8 2Breast Cancer- W 9 4Vote 16 6Labor 16 6Hepatitis 19 7Colic-Horse 22 6Ionosphere 34 9Lymph 18 7Audiology 69 20Dermatology 34 12Lung Cancer 32 25

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No. of Features selected

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Conclusion We have proposed an efficient feature selection

method based on SI and filter method techniques.

Proposed method is extensively experimented over anumber of benchmark datasets and classifiers.

ACO-SU yields better results as compared to otherSI based feature selection methods considered in the

study. ACO-SU outperformed other methods in terms of

predictive classification accuracy and number offeatures selected.

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Thank You

Questions?

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ICET Presentation 1

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