WITHDRAWN: A novel wrapper FS based on binary swallow swarm optimization with score-based criteria fusion for gene expression microarray data

Abstract

In recent days, Gene Expression Microarray (GEM) data increases its significance to diagnose the diseases appropriately. The key issue of analyzing gene expression information for huge amount of genes with less samples it to extract the disease-related data from the large number of unwanted information and noise. Feature choice, removing unwanted and inappropriate genes was a major process for investigating this challenge. So, the Feature Selection (FS) and categorization are the primary tasks in analyzing the GEM. In this article, a Swallow Swarm Optimization (SSO) with Score-based Criteria Fusion (Optimized SCF (OSCF)) wrapper FStechnique is developed to predict the cancer with a high classification efficiency. Novel wrapper FStechnique follows the procedure of Binary Weight SSO (BWSSO) and the fitness is evaluated by combining the SCF and classification accuracy. OSCF algorithm fuses SCF and classification accuracy to measure the genes significance and redundancy for verifying the genes dependency. It could improve the efficiency of the classifiers such as K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Recursive Neural Networks (RNN), and evaluate its improvement using four different GEM datasets (Prostate cancer, Small Round Blue Cell Tumours (SRBCT), Leukemia, and Lymphoma). Experimental results verify that the OSCF technique shows better results when compared to conventional techniques based on precision, recall, accuracy and Area Under Curve (AUC).