A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data

Abstract

Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima. Furthermore, highdimensional FS problems such as finding a small set of biomarkers to make a diagnostic call add an additional challenge as such methods ability to pick out the most important features must remain unchanged in decision spaces of increasing dimensions and presence of irrelevant features. We developed a combinatorial PSO algorithm, called COMB-PSO, that scales up to high-dimensional gene expression data while still selecting the smallest subsets of genes that allow reliable classification of samples. In particular, COMB-PSO enhances the encoding, speed of convergence, control of divergence and diversity of the conventional PSO algorithm, balancing exploration and exploitation of the search space. Applying our approach on real gene expression data of different cancers, COMB-PSO finds gene sets of smallest size that allow a reliable classification of the underlying disease classes.

A deep learning and genetic algorithm based feature selection processes on Leukemia Data

“…Dhrif et al [16] presented a new variant of the Particle Swarm Optimization (PSO) algorithm to increase the classification accuracy and preserve the acceptable dimensions of feature subsets when there are many uninformative data. For this purpose, a new encoding scheme is used for mapping particle positions to probabilities.…”