Support Vector Machine with Graphical Network Structures in Features

EasyChair Preprint no. 1277

14 pagesDate: July 10, 2019

Abstract

Machine learning techniques, regardless of being {\em supervised} or {\em unsupervised}, have attracted extensive research attention in handling data classification. Typically, among supervised machine learning algorithms, Support Vector Machine (SVM) and its extensions have been widely used in various areas due to their great prediction capability. These learning algorithms basically treat features of the instances independently when using them to do classification. However, in applications, features are commonly correlated with complex network structures. Ignoring such a characteristic and naively implementing the SVM algorithm often yield erroneous classification results. To address the limitation of the SVM algorithm, we propose new learning algorithms which accommodate network structures of the features of the instances. Our algorithms capitalize on graphical model theory and make use of the available R software package for SVM. The implementation of the proposed learning algorithms is computationally easy and fast. We apply the new algorithms to analyze the data arising from a gene expression study.

Keyphrases: Classification, graphical model, network structure, Support Vector Machine