Correlation kernels for support vector machines classification with applications in cancer data.
High dimensional bioinformatics data sets provide an excellent and challenging research problem in machine learning area. In particular, DNA microarrays generated gene expression data are of high dimension with significant level of noise. Supervised kernel learning with an SVM classifier was successfully applied in biomedical diagnosis such as discriminating different kinds of tumor tissues. Correlation Kernel has been recently applied to classification problems with Support Vector Machines (SVMs). In this paper, we develop a novel and parsimonious positive semidefinite kernel. The proposed kernel is shown experimentally to have better performance when compared to the usual correlation kernel. In addition, we propose a new kernel based on the correlation matrix incorporating techniques dealing with indefinite kernel. The resulting kernel is shown to be positive semidefinite and it exhibits superior performance to the two kernels mentioned above. We then apply the proposed method to some cancer data in discriminating different tumor tissues, providing information for diagnosis of diseases. Numerical experiments indicate that our method outperforms the existing methods such as the decision tree method and KNN method.
Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, Run Run Shaw Building, The University of Hong Kong, Pokfulam Road, Hong Kong.
Hao
H
Jiang
Wai-Ki
WK
Ching
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000378:metabolism; D003110:Colonic Neoplasms / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003661:Decision Support Techniques; D005260:Female; D005786:Gene Expression Regulation; D006801:Humans; D007938:Leukemia / Q000378:metabolism; D008962:Models, Theoretical; D009369:Neoplasms / Q000473:pathology / Q000628:therapy*; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine; D013997:Time Factors