Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

Abstract

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes.

Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data

“…We remark that the results obtained by our approach are very encouraging compared to previous work. Indeed, for most of the datasets examined, the classification accuracies obtained by the proposed gene selection method matched or outperformed those obtained using other methods [6, 21–25, 28].…”

“…In this section, we compare our method with eight recently referred algorithms in the literature [6, 21–25, 28]. And to make sense of this comparison, the experiments are performed under the same conditions in each algorithm.…”

“…The first works on the DNA microarray classification were published at the end of the 1990s [13, 14]. In this context, several researchers have utilized metaheuristic methods and the ACO algorithm for solving the feature selection problem (particularly gene selection), in order to facilitate recognition of cancer cells: ACO [15–20] algorithm, PSO [4, 6, 21–25] genetic algorithm [4, 26, 27], incorporating imperialist competition algorithm (ICA) [28], and binary differential evolution (BDE) algorithm [29].…”

Biomarker discovery by imperialist competitive algorithm in mass spectrometry data for ovarian cancer prediction

“…This algorithm was introduced in 2007,[ 24 ] and it has been used so far to solve many problems in the area of optimization. [ 25 26 27 28 29 30 31 32 33 34 35 36 37 ] Like other evolutionary algorithms, this algorithm is composed of the initial set of possible solutions, which of them is called a country. The ICA gradually improves these initial solutions (countries) and finally provides the desired answer to the optimization problem (the desired country).…”

A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques

“…For the cancer classification data on gene expression data, PSO and DT classifiers were implemented by Chen et al [ 32 ]. For gene selections, the various techniques reported in literature are utilizing multiobjective algorithms [ 33 ], a hybrid binary Imperialist Competition Algorithm (ICA), and tabu search approach [ 34 ], a binary differential evolution algorithm [ 35 ], a simplified swarm optimization using a Social Spider Optimization (SSO) algorithm [ 36 ], Artificial Bee Colony (ABC) [ 37 ], Binary PSO [ 38 ], novel rule-based algorithm [ 39 ], and Shuffled Leap Frog Algorithm (SLFA) [ 40 ], and it has been well explored. However, in this paper, other suitable swarm intelligence techniques have been explored and analyzed comprehensively.…”