Abstract: Microarray technology is universally used in the study
of disease diagnosis using gene expression levels. The main
shortcoming of gene expression data is that it includes thousands of
genes and a small number of samples. Abundant methods and
techniques have been proposed for tumor classification using
microarray gene expression data. Feature or gene selection methods
can be used to mine the genes that directly involve in the
classification and to eliminate irrelevant genes. In this paper
statistical measures like T-Statistics, Signal-to-Noise Ratio (SNR)
and F-Statistics are used to rank the genes. The ranked genes are used
for further classification. Particle Swarm Optimization (PSO)
algorithm and Shuffled Frog Leaping (SFL) algorithm are used to
find the significant genes from the top-m ranked genes. The Naïve
Bayes Classifier (NBC) is used to classify the samples based on the
significant genes. The proposed work is applied on Lung and Ovarian
datasets. The experimental results show that the proposed method
achieves 100% accuracy in all the three datasets and the results are
compared with previous works.
Abstract: Tumor classification is a key area of research in the
field of bioinformatics. Microarray technology is commonly used in
the study of disease diagnosis using gene expression levels. The
main drawback of gene expression data is that it contains thousands
of genes and a very few samples. Feature selection methods are used
to select the informative genes from the microarray. These methods
considerably improve the classification accuracy. In the proposed
method, Genetic Algorithm (GA) is used for effective feature
selection. Informative genes are identified based on the T-Statistics,
Signal-to-Noise Ratio (SNR) and F-Test values. The initial candidate
solutions of GA are obtained from top-m informative genes. The
classification accuracy of k-Nearest Neighbor (kNN) method is used
as the fitness function for GA. In this work, kNN and Support Vector
Machine (SVM) are used as the classifiers. The experimental results
show that the proposed work is suitable for effective feature
selection. With the help of the selected genes, GA-kNN method
achieves 100% accuracy in 4 datasets and GA-SVM method
achieves in 5 out of 10 datasets. The GA with kNN and SVM
methods are demonstrated to be an accurate method for microarray
based tumor classification.