Post on 04-Apr-2018
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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
Basic Ingredients of ACO
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Constraint Satisfaction and SolutionConstruction
Basic Ingredients of ACO
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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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Experimentation Framework
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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Experimentation Framework
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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