Artificial neural network classification of microarray data using new hybrid gene selection method

TLDR: ICA + ABC are a promising approach for solving gene selection and cancer classification problems using microarray data and the experimental results show that the proposed algorithm gives more accurate classification rate for ANN classifier.

Abstract: This paper proposed a new combination of feature selection/extraction approach for Artificial Neural Networks (ANNs) classification of high-dimensional microarray data, which uses an Independent Component Analysis (ICA) as an extraction technique and Artificial Bee Colony (ABC) as an optimisation technique. The study evaluates the performance of the proposed ICA + ABC algorithm by conducting extensive experiments on five-binary and one multi-class gene expression microarray data set and compared the proposed algorithm with ICA and ABC. The proposed method shows superior performance as it achieves the highest classification accuracy along with the lowest average number of selected genes. Furthermore, the present work compares the proposed ICA + ABC algorithm with popular filter techniques and with other similar bio-inspired algorithms with ICA. The experimental results show that the proposed algorithm gives more accurate classification rate for ANN classifier. Therefore, ICA + ABC are a promising approach for solving gene selection and cancer classification problems using microarray data.