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

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 fivebinary 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.

Feature dimensionality reduction: a review

“…To select informative genes based on a Naïve Bayes (NB) algorithm, a new hybrid search technique is proposed by ICA and ABC [125]. The other new combination of feature selection/extraction approach is designed for Artificial Neural Networks (ANNs) classification of high-dimensional microarray data with the help of ICA and ABC by Aziz et al [126].…”

Supervised learning of COVID-19 patients' characteristics to discover symptom patterns and improve patient outcome prediction

“…The proposed approach consists of two steps: 1. features extraction based on independent component analysis (ICA) method, and 2. selecting the optimal set of genes based on artificial bee colony (ABC) theory. The authors claimed that the proposed method reveals superior performance than the state-of-the-art approaches developed for selecting optimal genes from microarray data for the Naïve Bayes [ 46 ] and artificial neural network [ 47 ] classification methods.…”

Hybrid Feature Selection and Ensemble Learning Methods for Gene Selection and Cancer Classification

“…However, the tested classifiers were under tree family, and other kinds of classifiers were not assessed [29]. Along this line, the assessment of different classifiers such as artificial neural network (ANN) [30] and www.ijacsa.thesai.org fuzzy decision tree algorithm [31] has been made upon microarray data. In addition, the two evolutionary algorithms of PSO and GA are usually used in wrapper form [17,20].…”