Stability of Indexed Microarray and Text Data

Abstract

The common challenge for machine learning and data mining tasks is the curse of High Dimensionality. Feature selection reduces the dimensionality by selecting the relevant and optimal features from the huge dataset. In this research work, a clustering and genetic algorithm based feature selection (CLUST-GA-FS) is proposed that has three stages namely irrelevant feature removal, redundant feature removal, and optimal feature generation. The performance of the feature selection algorithms are analyzed using the parameters like classification accuracy, precision, recall and error rate. Recently, an increasing attention is given to the stability of feature selection algorithms which is an indicator that requires that similar subsets of features are selected every time the algorithm is executed on the same dataset. This work analyzes the stability of the algorithm on four publicly available dataset using stability measurements Average normal hamming distance(ANHD), Dices coefficient, Tanimoto distance, Jaccards index and Kuncheva index.