Multiclass classification on brain cancer with multiple support vector machine and feature selection based on kernel function
Zuherman RustamSelly Anastassia Amellia Kharis
Abstract
Cancer is one disease that needs a proper treatment. There are more than 150 types of cancer, one of them is brain cancer. Taking advantage from microarray data, machine learning methods can be applied to help brain cancer prediction according to its type. This problem can be referred to as a multiclass classification problem. Using one versus one approach, the multiclass problem with k classes can be transformed into k(k+1)/2 binary class classification. To improve the accuracy, the features candidate will be evaluated using feature selection. In this research, Kernel Function is implemented as the feature selection method and Multiple Support Vector Machine (MSVM) method is implemented as the classification method. The results obtained showed the comparison accuracy of MSVM use and without feature selection.
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