Ensamble based multi filters algorithm for tumor classification in high dimensional microarray dataset

Abstract

Prevalent adoption of machine learning has magnified its requirements in high dimensional microarray data classification. Due to explosive increase of data dimensionality, the existence of features redundancy and ambiguity directly leads to classification inaccuracy. Filter feature selection algorithms are capable to boost classification accuracy and diminish computational complexity by extracting relevant information through supervised learning. However, the independent filter algorithm is incompetent to consider the features interaction which resulting an imbalance selection of significant features and consequently degrading the classifier performance. This paper presents an assemblage of multi filters algorithm which assembles four filters algorithm outputs with frequency of occurrence rate evaluation to improve classification performance by attaining an optimal number of significant features. Experimental analysis was performed on a standard Breast Cancer dataset consists of 286 instances and Support Vector Machine (SVM) classifier. The experimental results proved that the ensemble based multi filters algorithm with occurrence rate evaluation successfully depletes from 9 original dataset features to 5 optimal significant features. The finding indicates that this technique competently signifies SVM classification performance in terms of accuracy with optimum significant features for high dimensional microarray data compared to independent filter algorithm.