Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification

Main Article Content

Noramalina Mohd Hatta
Zuraini Ali Shah
Shahreen Kasim


Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here, three basic kernel functions was used and tested with selected dataset and they are linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel function. The three kernels were tested by different dataset to gain the accuracy. For a comparison, this study conducting a test by with and without feature selection in SVM classification kernel function since both tests will give different result and thus give a big meaning to the study.

Article Details

How to Cite
N. Mohd Hatta, Z. Ali Shah, and S. Kasim, “ Evaluate the Performance of SVM Kernel Functions for Multiclass Cancer Classification ”, Int. J. Data. Science., vol. 1, no. 1, pp. 37-41, May 2020.


Deb K., and Reddy A. R. (2003). Reliable classification of two-class cancer data using evolutionary algorithms. BioSystems, 72(1), 111-129.

George G., and Raj V. C. (2011). Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profile.arXiv preprint arXiv:1109.1062.

Pomeroy S. L., Tamayo P., Gaasenbeek M., Sturla L. M., Angelo M., McLaughlin M. E.,and Golub T. R. (2002). Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature,415(6870), 436-442.

Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C. H., Angelo M., and Golub T. R. (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences,98(26), 15149-15154.

Shipp M. A., Ross K. N., Tamayo P., Weng A. P., Kutok J. L., Aguiar R. C., and Ray T. S. (2002). Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine, 8(1), 68-74.

Staunton J. E., Slonim D. K., Coller H. A., Tamayo P., Angelo M. J., Park J., and Mesirov J. P. (2001). Chemosensitivity prediction by transcriptional profiling. Proceedings of the National Academy of Sciences,98(19), 10787-10792.