Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine. (1st July 2020)

• Record Type: Journal Article
• Authors: Nguyen, Tram Thi Huyen; Nguyen, Pol Van; Tran, Quang Vinh; Vo, Nam Xuan; Vo, Trung Quang
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 36:Number 9(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-01
• Subjects: kernel extreme learning machine -- dataset -- gene selection -- microarray -- modified firefly based discriminant independent component analysis; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3372
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: One of the challenging tasks in the medicinal field is genomic disorder investigation and its classification from the microarray dataset. The microarray dataset reorganization and its classification is more complex and expensive in the biomedical research area due to the larger number of features in the microarray dataset. In this paper, we construct a hybrid feature selection method such as t test, Fisher ration, and Bayesian logistic regression to select genes and that reduce the time cost. Based on the features, the top‐ranked features are selected via the best hybrid rank method. Thereafter, the features are extracted using the modified firefly optimization‐based discriminant independent component analysis (MF‐DICA). Especially, the modified firefly optimization algorithm is capable of improving the search efficiency of DICA. From the high dimensional microarray dataset, MF‐DICA is used to obtain the best features within the entire search space. The kernel extreme learning machine classifies the gene features depending upon the most relevant class. Experimentally, six datasets namely Leukemia dataset, Diffuse Larger B‐cell Lymphomas, Lung cancer, Breast cancer, Prostate tumor, and Colon dataset are chosen to evaluate the performance of proposed approaches. Finally, the experimental data demonstrate that the proposed method is well suitable to classify the microarray data. Abstract : Hybrid gene selection approach is used to select the genes. Features are extracted using Modified Firefly Optimization‐based Discriminant Independent Component Analysis. Kernel extreme learning machine is explored. The proposed KELM classifier performance delivers optimal microarray data classification.