mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

الملخص EN

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach.

In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile.

In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile.

The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes.

We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets.

Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques.

We reimplemented two of these techniques for the sake of a fair comparison using the same parameters.

These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO).

The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods.

This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.