A Modified Firefly Deep Ensemble for Microarray Data Classification

Abstract

Abstract Many researchers are using microarray technology to examine and investigate the levels of gene expression in a specific organism, which is an emerging trend in the field of genetic research. Microarray studies have a wide range of applications in the health sector, including disease prediction and diagnostics, as well as cancer research. Due to the existence of irrelevant or duplicated data in microarray datasets, it is difficult to correctly and immediately capture possible patterns using existing algorithms. Feature selection (FS) has evolved into a critical approach for identifying and eliminating the most pertinent qualities. The enormous dimensionality of microarray datasets, on the other hand, presents a significant barrier to the majority of available FS techniques. In this research, we propose a Modified Firefly Feature Selection (MFFS) algorithm that will reduce the irrelevant attributes needed for classification and a Deep Learning Model for classifying the microarray data. The experimental outcomes show that the proposed MFFS algorithm combined with a Hybrid Deep Learning Algorithm outperforms the existing methods in terms of feature set size, accuracy, precision, recall, F-measure and AUC for a dataset with larger number of features.