A comparative analysis of swarm intelligence techniques for feature selection in cancer classification.

Chellamuthu Gunavathi,Kandasamy Premalatha

doi:10.1155/2014/693831

Chellamuthu Gunavathi, Kandasamy Premalatha

Open Access

https://doi.org/10.1155/2014/693831

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Abstract

Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL.

Highlights

Abundant methods and techniques have been proposed for cancer classification using microarray gene expression data
These results show that for Colon Tumor and Prostate Cancer the 100% accuracy is not achieved by any method
Cancer classification using gene expression data is an important task for addressing the problem of cancer prediction and diagnosis

Summary

Introduction

Abundant methods and techniques have been proposed for cancer classification using microarray gene expression data. Gene expression profiling by microarray method has appeared as a capable technique for classification and diagnostic prediction of cancer. The informative genes are identified based on their T-statistics, SNR, and F-test values. The works related to gene selection and cancer classification using microarray gene expression data are discussed. Rough set concept with dependent degrees was proposed by Wang and Gotoh [6] In this method they screened a small number of informative single gene and gene pairs on the basis of their dependent degrees. A new ensemble gene selection method was applied by Liu et al [9] to choose multiple gene subsets for classification purpose, where the significant degree of gene was measured by conditional mutual information or its normalized form.

Swarm Intelligence Techniques

Feature Selection Based on Swarm Intelligence Techniques

Experimental Setup

Methodology

Experimental Results and Discussion

Conclusions