Abstract

Microarrays can be utilized to determine the comparative amount of particular mRNAs in two or more tissue samples for thousands of genes concurrently. As the supremacy of this technique has been identified, various open queries arise about suitable examination of microarray data. The multicategory cancer classification is playing a vital role in the field of medical sciences. As the numbers of cancer victims are increasing steadily, the necessity of the cancer classification techniques has become indispensible. In this research, initially preprocessing and normalization process is carried out to select the best gene datasets. Then, a combination of Advanced Integer-Coded Genetic Algorithm (AICGA) and Extreme Learning Machine (ELM), with refined group search optimizer (RGSO) technique is used for gene selection and cancer classification. AICGA is used with RGSO Based ELM classifier to choose an optimal set of genes which results in an efficient hybrid algorithm that can handle sparse data and sample imbalance. The refined group search optimizer based extreme learning machine is used to carry out the classification process. In the proposed RGSO based ELM, the weights and bias to ELM are optimized using RGSO for better simplification and classification of large value of gene datasets. The performance of the proposed approach is evaluated and the results are compared with existing methods. The proposed approaches are applied for real time R. Balakrishnan, Assistant Professor, Dr.NGP Arts and Science College, Coimbatore. E-mail:balakrishnan.scholar@yahoo.com Thirunavu Karthikeyan, Associate Professor, P.S.G. College of Arts and Science, Coimbatore. E-mail:t.karthikeyan.gasc@gmail.com datasets and benchmark datasets taken from dataset repositories.

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