Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance

Objective: The objective of this research article is to present a novel feature selection strategy for improving the classification performance over high dimensional data sets. Curse of dimensionality is the most serious downside of microarray data as it has more number of genes(features). This leads to discouraged computational stability. In microarray data analytics, identifying more relevant features required full attention. Most of the researchers applied two stage strategy for gene expression data analysis. In first stage, feature selection or feature extraction is employed as a preprocessing step to pinpoint more prominent features. In second stage, classification is applied using selected subset of features. Method: In this research also we followed the same strategy. But, we tried to introduce a distributed feature selection(dfs) strategy using Symmetrical Uncertainty(SU) and Multi Layer Perceptron(MLP) by distributing across the multiple clusters. Each cluster is equipped with finite number of features in it. MLP is employed over each cluster, and based on the highest accuracy and lowest Root Mean Square error rate(RMS) dominant cluster is nominated. Result: Classification accuracy with Ridor, Simple Cart (SC), KNN, SVM are measured by considering dominant cluster's features. The performance of this cluster is compared with the traditional filter based ranking techniques like Information Gain(IG), Gain Ratio Attribute Evaluator(GRAE), Chi-Squared Attribute Evaluator (Chi). The proposed method is recorded approximately 57% success rate, 18% competitive rate against traditional methods after applying it over 7 well high dimensional and one lower dimension dataset. Conclusion: The proposed methodology applied over very high dimensional microarry datasets. Using this method memory consumption will be reduced and classification performance can be improved.