Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
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Date
2021Author
Alomari, Osama AhmadMakhadmeh, Sharif Naser
Al-Betar, Mohammed Azmi
Alyasseri, Zaid Abdi Alkareem
Abu Doush, Iyad
Abasi, Ammar Kamal
Awadallah, Mohammed A.
Abu Zitar, Raed
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DNA microarray technology is the fabrication of a single chip to contain a thousand genetic codes.
Each microarray experiment can analyze many thousands of genes in parallel. The outcomes of the
DNA microarray is a table/matrix, called gene expression data. Pattern recognition algorithms are
widely applied to gene expression data to differentiate between health and cancerous patient samples.
However, gene expression data is characterized as a high dimensional data that typically encompassed
of redundant, noisy, and irrelevant genes. Datasets with such characteristics pose a challenge to
machine learning algorithms. This is because they impede the training and testing process and entail
high resource computations that deteriorate the classification performance. In order to avoid these
pitfalls, gene selection is needed. This paper proposes a new hybrid filter-wrapper approach using
robust Minimum Redundancy Maximum Relevancy (rMRMR) as a filter approach to choose the topranked genes. Modified Gray Wolf Optimizer (MGWO) is used as a wrapper approach to seek further
small sets of genes. In MGWO, new optimization operators inspired by the TRIZ-inventive solution are
coupled with the original GWO to increase the diversity of the population. To evaluate the performance
of the proposed method, nine well-known microarray datasets are tested. The support vector machine
(SVM) is employed for the classification task to estimate the goodness of the selected subset of
genes. The effectiveness of TRIZ optimization operators in MGWO is evaluated by investigating the
convergence behavior of GWO with and without TRIZ optimization operators. Moreover, the results of
MGWO are compared with seven state-of-art gene selection methods using the same datasets based on
classification accuracy and the number of selected genes. The results show that the proposed method
achieves the best results in four out of nine datasets and it obtains remarkable results on the remaining
datasets. The experimental results demonstrated the effectiveness of the proposed method in searching
the gene search space and it was able to find the best gene combinations.
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