Supervised classification using hybrid probabilistic decision graphs

被引：0
作者：
Fernández, Antonio [1 ]
Rumí, Rafael [1 ]
del Sagrado, José [2 ]
Salmerón, Antonio [1 ]
机构：
[1] Department of Mathematics, University of Almería, Ctra. Sacramento s/n, Almería
[2] Department of Computer Science, University of Almería, Ctra. Sacramento s/n, Almería
来源：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8754卷
关键词：
Mixtures of polynomials; Mixtures of truncated basis functions; Mixtures of truncated exponentials; Probabilistic decision graphs; Supervised classification;
D O I：
10.1007/978-3-319-11433-0_14
中图分类号：
学科分类号：
摘要：
In this paper we analyse the use of probabilistic decision graphs in supervised classification problems. We enhance existing models with the ability of operating in hybrid domains, where discrete and continuous variables coexist. Our proposal is based in the use of mixtures of truncated basis functions. We first introduce a new type of probabilistic graphical model, namely probabilistic decision graphs with mixture of truncated basis functions distribution, and then present an initial experimental evaluation where our proposal is compared with state-of-the-art Bayesian classifiers, showing a promising behaviour. © Springer International Publishing Switzerland 2014.
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页码：206 / 221
页数：15
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