A review of feature selection methods in medical applications.

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature.

Binary grasshopper optimisation algorithm approaches for feature selection problems

Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification

Feature selection using bare-bones particle swarm optimization with mutual information

The emergence of DNA Microarray technology has enabled researchers to analyze the expression level of thousands of genes simultaneously. The Microarray data analysis is the process of finding the most informative genes as well as remove redundant and irrelevant genes. One of the most important applications of the Microarray data analysis is cancer classification. However, the curse of dimensionality and the curse of sparsity make classifying gene expression profiles a challenging task. One of the most effective methods to overcome these challenges is feature (gene) selection. In this paper, we aim to review and compare the most recent hybrid approaches that employ bio-inspired evolutionary methods as the wrapper method.

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Junying Chen

Papers

A hybrid feature selection algorithm for gene expression data classification