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

Zhang, Yi; Ren, Jinchang; Jiang, Jianmin

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.

Citation

ZHANG, Y., REN, J. and JIANG, J. 2015. Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations. Computational intelligence and neuroscience [online], 2015, article ID 423581. Available from: https://doi.org/10.1155/2015/423581

• Journal Article Type: Article
• Acceptance Date: May 11, 2015
• Online Publication Date: May 21, 2015
• Publication Date: Dec 31, 2015
• Deposit Date: Jul 29, 2024
• Publicly Available Date: Jul 29, 2024
• Journal: Computational intelligence and neuroscience
• Print ISSN: 1687-5265
• Electronic ISSN: 1687-5273
• Publisher: Hindawi
• Peer Reviewed: Peer Reviewed
• Volume: 2015
• Article Number: 423581
• DOI: https://doi.org/10.1155/2015/423581
• Keywords: Machine learning; Maximum likelihood classification (MLC); Support vector machines (SVM); Bayesian statistics
• Public URL: https://rgu-repository.worktribe.com/output/2059466