IMPROVING DETECTION PERFORMANCE OF ARTIFICIAL NEURAL NETWORK BY SHAPLEY VALUE EMBEDDED GENETIC FEATURE SELECTOR

This work is motivated by the interest in feature selection that greatly affects the detection accuracy of a classifier. The goals of this paper are (i) identifying optimal feature subset using a novel wrapper based feature selection algorithm called Shapley Value Embedded Genetic Algorithm (SVEGA),...

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Bibliographic Details
Published inNeural Network World Vol. 26; no. 2; pp. 175 - 201
Main Authors Sasikala, S., Appavu alias Balamurugan, S., Geetha, S.
Format Journal Article
LanguageEnglish
Published Prague Institute of Information and Computer Technology 01.01.2016
Czech Technical University in Prague, Faculty of Transportation Sciences
Subjects
Accuracy
Algorithms
Classifiers
Data mining
Genetic algorithms
Learning theory
Medical
Neural networks
Repositories
genetic algorithm
artificial neural network
classification
shapley values
feature selection
medical data mining
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Summary:This work is motivated by the interest in feature selection that greatly affects the detection accuracy of a classifier. The goals of this paper are (i) identifying optimal feature subset using a novel wrapper based feature selection algorithm called Shapley Value Embedded Genetic Algorithm (SVEGA), (ii) showing the improvement in the detection accuracy of the Artificial Neural Network (ANN) classifier with the optimal features selected, (iii) evaluating the performance of proposed SVEGA-ANN model on the medical datasets. The medical diagnosis system has been built using a wrapper based feature selection algorithm that attempts to maximize the specificity and sensitivity (in turn the accuracy) as well as by employing an ANN for classification. Two memetic operators namely ＂include＂ and ＂remove＂ features (or genes) are introduced to realize the genetic algorithm (GA) solution. The use of GA for feature selection facilitates quick improvement in the solution through a fine tune search. An extensive experimental evaluation of the proposed SVEGA-ANN method on 26 benchmark datasets from UCI Machine Learning repository and Kent ridge repository, with three conventional classifiers, outperforms state-of-the-art systems in terms of classification accuracy, number of selected features and running time.
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content type line 23
ISSN:1210-0552
2336-4335
DOI:10.14311/NNW.2016.26.010