Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations

Yi Zhang, Jinchang Ren, Jianmin Jiang

Research output: Contribution to journalArticle

13 Citations (Scopus)

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.

Accepted on May 11, 2015
LanguageEnglish
Article number423581
Number of pages8
JournalComputational Intelligence and Neuroscience
Volume2015
DOIs
Publication statusPublished - 2015

Fingerprint

Decision Support Techniques
Maximum likelihood
Maximum Likelihood
Support vector machines
Support Vector Machine
Decision Making
Classifiers
Decision making
Classifier
Evaluation
Learning
Sonar
Performance Assessment
DNA sequences
Nonparametric Methods
Statistical Models
Machine Learning
Learning Process
Breast Cancer
Probabilistic Model

Keywords

  • maximum likelihood classifier
  • support vector machines
  • machine
  • decision making

Cite this

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Combining MLC and SVM classifiers for learning based decision making : analysis and evaluations. / Zhang, Yi; Ren, Jinchang; Jiang, Jianmin.

In: Computational Intelligence and Neuroscience, Vol. 2015, 423581, 2015.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Combining MLC and SVM classifiers for learning based decision making

T2 - Computational Intelligence and Neuroscience

AU - Zhang, Yi

AU - Ren, Jinchang

AU - Jiang, Jianmin

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