Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.

Yi Zhang,Jinchang Ren,Jianmin Jiang

doi:10.1155/2015/423581

Abstract

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

Highlights

Maximum likelihood classification (MLC) is one of the most commonly used approaches in signal classification and identification, which has been successfully applied in a wide range of engineering applications including classification for digital amplitude-phase modulations [1], remote sensing [2], genes selection for tissue classification [3], nonnative speech recognition [4], chemical analysis in archaeological applications [5], and speaker recognition [6]
Results from a radial basis function (RBF)-Kernelled support vector machines (SVM) and the MLC. In this group of experiments, the RBF kernel is used for the SVM in the combined classifier as it is popularly used in various classification problems [16, 22]
RBF-kernelled SVM (R-SVM) produces much improved results compared to those using linear SVM, especially for the training results

Summary

Introduction

Maximum likelihood classification (MLC) is one of the most commonly used approaches in signal classification and identification, which has been successfully applied in a wide range of engineering applications including classification for digital amplitude-phase modulations [1], remote sensing [2], genes selection for tissue classification [3], nonnative speech recognition [4], chemical analysis in archaeological applications [5], and speaker recognition [6]. Taking the application in remote sensing, for example, in Pal and Mather [12] and Huang et al [13], it is found that SVM outperforms MLC and several other classifiers. In Waske and Benediktsson [14], SVM produces better results from SAR images, yet in most cases it generates worse results than MLC from TM images. In Szuster et al [15], SVM only yields slightly better results than MLC for land cover analysis. As a result, detailed assessments as on what conditions SVM outperforms or appears inferior to MLC are worth further investigation

Objectives

Results

Conclusion