The Classification of Cancer Gene using Hybrid Method of Machine Learning

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Deepak Kumar Gour
Y. K. Jain, Geetika S Pandey


The purpose of this research work is to develop a method of classifying cancer using Gene expression data that is being used to gather
information from tissue samples is expected to significantly improve the development of efficient cancer diagnosis and to provide understanding and
insight into cancer related cellular processes. In this research, we propose a method for selection which uses factor analysis to further improve the
SVM-based classification performance of gene expression data. We examine two sets of published gene expression data to validate the new feature
selection method by means of Machine Learning with Binary Classification i.e. SVM classifier with different parameters. Experiments show that the
proposed method can select a small quantity of principal factors to represent a large number of genes and SVM has a superior classification
performance with the common factors which are extracted from gene expression data. Moreover, experiment results demonstrate successful cross
validation accuracy of 93.75% for the breast cancer dataset and 98% for the leukemia dataset.




Keywords: Data mining, Support Vector Machine, Gene Expression Profile, Factor Analysis.


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