Comparing Multiobjective Evolutionary Algorithms For Cancer Data Microarray Feature Selection

Microarray analysis has gradually becoming an important tool for diagnosis and classification of human cancers. Microarray data consists of thousands of features most of which have been irrelevant for classifying microarray gene expression patterns. The election of a minimal subset of features for classification is a challenging task. In this work, a deep analysis and comparison of multiobjective evolutionary algorithms (MOEAs) for Feature Selection of cancer microarray dataset has been presented. The experiments have been carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma, and Leukaemia available in the literature. A microarray data preprocessing is carried out in order to remove strongly correlated features. A detailed comparative study has been made to analyze the results of the different MOEAs.