Affiliations: [a] Department of Computer Science, The S.F.R College for Women, Sivakasi, Tamil Nadu, India | [b] Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India | [c] Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India
Correspondence:
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Corresponding author: C. Devi Arockia Vanitha, Department of Computer Science, The S.F.R College for Women, Sivakasi 626123, Tamil Nadu, India. Tel.: +94 864 62030; E-mail:vanima_c@yahoo.co.in
Abstract: Gene expression profiles have been used for Cancer Classification recently.
In this work, the multi-SVM (Support Vector Machine) approach with a novel
Gene selection method using Mutual Information (MI) is developed for
multi-class classification in the cancer diagnosis area. The mutual
information between genes and class label is computed and used for
identifying the discriminating genes in each category. All the genes are
assigned rank based on their mutual information value and the optimal number
of genes with the highest values are chosen and fed into the classifier. The
multi-SVM classifier constructs separate classifier for each class and the
combined multi-class classifier assigns a tissue sample to the class with
the highest support. The performance of the proposed Multiclass Support
Vector Machine (mSVM) with Gene Selection using the mutual information
approach is evaluated on four benchmark gene expression datasets for cancer
diagnosis, namely, the Leukemia dataset, the Lymphoma dataset, the NCI60
dataset and the GCM dataset. The multi-SVM approach develops the most
effective classifier in achieving an accurate cancer diagnosis by analyzing
gene expression data and it outperforms other popular machine learning
algorithm like k-Nearest Neighbor. From the simulation study it is observed
that the proposed approach reduces the dimension of the input features by
identifying the most discriminating gene subset for each category and
improves the predictive accuracy for multi-class cancer.
