An analysis of expression data using a support vector machine and dimensional reduction methods

Abstract

Gene expression data are expected to be a significant aid in the development of efficient cancer diagnosis and classification platforms. However, gene expression data are high-dimensional and the number of samples is small in comparison to the dimensions of the data. Furthermore, the data are inherently noisy. Therefore, in order to improve the accuracy of the classifiers, we would be better off reducing the dimensionality of the data. As a method of dimensionality reduction, there are two previous proposals: feature selection and dimensionality reduction. Feature selection is a feedback method which incorporate the classifier algorithm in the future selection process. Dimensionality reduction refers to algorithms and techniques which create new attributes as combinations of the original attributes in order to reduce the dimensionality of a data set. In this article, we compared the feature selection methods and the dimensionality reduction methods, and verified the effectiveness of both types. For the feature selection methods we used one previously known method and three proposed methods, and for the dimensionality reduction methods we used one previously known method and one proposed method. From an experiment using a benchmark data set, we confirmed the effectiveness of our proposed method with each type of dimensional reduction method.