TY  - EJOU
AU  - Gulande, Punam 
AU  - Awale, R. N. 

TI  - Microarray Gene Expression Classification: An Efficient Feature Selection Using Hybrid Swarm Intelligence Algorithm
T2  - Computer Systems Science and Engineering

PY  - 2024
VL  - 48
IS  - 4
SN  - 

AB  - The study of gene expression has emerged as a vital tool for cancer diagnosis and prognosis, particularly with the advent of microarray technology that enables the measurement of thousands of genes in a single sample. While this wealth of data offers invaluable insights for disease management, the high dimensionality poses a challenge for multiclass classification. In this context, selecting relevant features becomes essential to enhance classification model performance. Swarm Intelligence algorithms have proven effective in addressing this challenge, owing to their ability to navigate intricate, non-linear feature-class relationships. This paper introduces a novel hybrid swarm algorithm, fusing the capabilities of the Artificial Bee Colony (ABC) and Firefly algorithms, aimed at improving feature selection in gene expression classification. The proposed method undergoes rigorous validation through statistical machine learning techniques and quantitative parameter evaluation, with comprehensive comparisons to established techniques in the field. The findings underscore the superiority of the hybrid Swarm Intelligence approach for feature selection in gene expression classification, offering promising prospects for enhancing cancer diagnosis and prognosis.
KW  - Artificial bee colony (ABC); firefly algorithm (FA); swarm intelligence (SI); artificial neural network (ANN); machine learning

DO  - 10.32604/csse.2024.046123