Multi-dimensional Bayesian Network Classifier Trees

被引：5

作者：

Gil-Begue, Santiago ^{[1
]}

Larranaga, Pedro ^{[1
]}

Bielza, Concha ^{[1
]}

机构：

[1] Univ Politecn Madrid, Madrid, Spain

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2018, PT I | 2018年 / 11314卷

关键词：

Multi-dimensional and multi-label supervised classification problems; Bayesian networks; Classification trees; Meta-classifiers; Hybrid classifiers; Performance evaluation measures;

D O I：

10.1007/978-3-030-03493-1_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models tailored to solving multi-dimensional classification problems, where an instance has to be assigned to multiple class variables. In this paper, we propose a novel multi-dimensional classifier that consists of a classification tree with MBCs in the leaves. We present a wrapper approach for learning this classifier from data. An experimental study carried out on randomly generated synthetic data sets shows encouraging results in terms of predictive accuracy.

引用

页码：354 / 363

页数：10

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