A tumor classification model using least square regression

Abstract

An accurate tumor classification is important to diagnosis and treatment cancers. The conventional methods for tumor classification include training and testing phases, which may cause over fitting. Although this problem can be avoided by using sparse representation classification, the existing sparse representation methods for tumor classification are inefficient. In this paper, an efficient and robust classification model LSRC based on least square regression and nearest subspace rule is adopted for tumor classification. To investigate its performance, our proposed model LSRC is compared with 3 existing methods on 9 tumor datasets. The experimental results show that our proposed model can use less time to achieve higher classification accuracy.