Biogeography-Based Informative Gene Selection and Cancer Classification Using SVM and Random Forests

TL;DR

This paper introduces hybrid biogeography-based optimization methods combined with SVM and Random Forests for gene selection in cancer classification, achieving comparable accuracy on high-dimensional microarray data.

Contribution

It presents novel hybrid algorithms integrating biogeography-based optimization with gene ranking heuristics for improved cancer classification.

Findings

Achieved classification accuracy comparable to existing algorithms.

Effectively reduced gene set size while maintaining performance.

Validated on multiple cancer gene expression datasets.

Abstract

Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranking as a heuristic, for microarray gene expression analysis. This heuristic is obtained from information gain filter ranking procedure. The BBO algorithm generates a population of candidate subset of genes, as part of an ecosystem of habitats, and employs the migration and mutation processes across multiple generations of the population to improve the classification accuracy. The fitness of each gene subset is assessed by the classifiers - SVM and Random Forests. The performances of these hybrid techniques are evaluated on three cancer gene expression datasets retrieved from the Kent Ridge Biomedical datasets collection and the libSVM data repository. Our results demonstrate that genes selected by the proposed techniques yield classification accuracies comparable to previously reported algorithms.