In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection is much needed to solve the high dimensionality issue in microarray data, small sample size, and noisy. The best way to classify cancer is to select those genes that hold the most informative ones, and this process contributes significantly to the classification performance of microarrays. In this survey, we comprehensively studied hybrid selection methods proposed since 2017 that may be used for comparison to several other algorithms proposed for gene selection in cancer classification in the past and looked to see if there are any challenges future authors that need to be discussed.

This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with k-nearest neighbors (k-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and k-NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes.

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