A TOPSIS based Method for Gene Selection for Cancer Classification

Abstract

Cancer classification based on microarray gene expressions is an important problem. In this work a new gene selection technique is proposed. The technique combines TOPSIS (Techniques for Order Preference by Similarity to an Ideal Solution) and F-score method to select subset of relevant genes. The output of the combined gene selection technique is fed into four different classifiers resulting in four hybrid cancer classification systems. In the proposed technique some important genes were chosen from thousands of genes (most informative genes). After that, the microarray data sets were classified with a K-Nearest Neighbour (KNN), Decision Tree (DT), Support Vector Machine (SVM) and Naive Bayes (NB).The goal of this proposed approach is to select most informative subset of features/genes that give better classification accuracy.