Affiliations: [a] Department of Computer Science and Information Technology, Siksha O'Anusandhan University, Bhubaneswar-751030, Odisha, India | [b] Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar-751024, Odisha, India
Correspondence:
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Corresponding author: Rasmita Dash, Department of Computer Science and Information Technology, Siksha, O'Anusandhan University, Bhubaneswar-751030, Odisha, India. E-mail:[email protected]
Abstract: Sample classification is a most critical task in
microarray data analysis. But representation of microarray data with the
huge search space of thousands of gene makes this work more complex
and difficult. To handle this problem both an efficient gene selection
technique and efficient classifier is required. In this paper, we propose a
multi-criterion Pareto differential evolution technique for feature
selection. This technique first uses a wrapper technique i.e. a population
based differential evolution gene selection (DEGS) algorithm for feature
selection. The motivation of choosing differential evolution as compared to
other learning technique is it tries to assign optimal ranks to each gene
using probability distribution factor present in the microarray dataset
using classification error as the fitness function. It is observed that
these selections contain relevant genes as well as some irrelevant genes. So
in the second phase bi-objective filter technique, called as Pareto based
optimization is used to select minimum number of top ranked genes in the
feature selection. Here we have considered information gain (IG) and Signal
to noise ratio (SNR) as two objective functions for Pareto optimization. To
verify the importance and relevance of the selected genes, classification
using K-nearest neighbour (KNN), naïve Bayesian classifier (NB),
artificial neural network (ANN) and support vector machine (SVM) is done.
Our experiment is conducted over four well known microarray dataset. The
experimental work shows that the proposed method is better than the existing
searching method in terms of both classification error and predicted feature
sets. The classification result shows that performance of SVM classifier is
better than the result obtained using KNN, NB and ANN. Finally this method
highlights its performance in terms of both relevance of genes and
classification output.
