Feature reduction for classifying high dimensional data sets using support vector machine

고차원 데이터의 분류를 위한 서포트 벡터 머신을 이용한 피처 감소 기법

We suggest a feature reduction method to classify mouse function data sets, which integrate several biological data sets represented as high dimensional vectors. To increase classification accuracy and decrease computational overhead, it is important to reduce the dimension of features. To do this, we employed Hybrid Huberized Support Vector Machine with kernels used for a kernel logistic regression method. When compared to support vector machine, this a pproach shows the better accuracy with useful features for each mouse function.