title: Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations.
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.
authors.affiliation: School of Computer Software, Tianjin University, Tianjin 300072, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Centre for Excellence in Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK.
authors.forename: Jinchang
authors.initials: J
authors.lastname: Ren
authors.affiliation: School of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
authors.forename: Jianmin
authors.initials: J
authors.lastname: Jiang
mesh_terms: D001483:Base Sequence; D001943:Breast Neoplasms / Q000145:classification; D016208:Databases, Factual / Q000706:statistics & numerical data; D003657:Decision Making / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D016013:Likelihood Functions; D000069550:Machine Learning; D008297:Male