A NOVEL MICROARRAY GENE SELECTION AND CLASSIFICATION USING INTELLIGENT DYNAMIC GREY WOLF OPTIMIZATION

Effective diagnosis of cancer in the medical field is very important to specific treatment. Exact prediction of different cancer types will provide a better treatment and minimization of toxicity in patients. Microarray high dimensionality of gene expression dataand large number of genes against small sample size, noise and repetition in datasets are the main issues which lead to poor classification accuracy. The selection of informative genes and to reduce dimensionality, Gene Selection technique is used in Microarray. In this paper, a novel meta-heurists algorithm based on Grey Wolf Optimization (GWO) and Artificial Intelligence (AI) is combined to design a model for cancer classification. This proposed work consists of two stages. First, a filter method such as Laplacian and Fisher score, are applied to extract the significant subset of features for faster classification and then Intelligent Dynamic Grey Wolf Optimization (IDGWO) is employed to identify the relevant genes. GWO is a swarm-based algorithm selected for gene expression data classification problem, because it makes classification easy about training and testing cancer data. The significant differences between filter methods of datasets are found by using several analyses. The proposed method was applied on five benchmark datasets by considering top 100 ranked genes selected by fisher score in Lymphoma and SRBCT that had a 100% performance using the IDGWO classifier.