Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Wang, Limina; b | Fan, Hangqia; * | Kong, Hea
Affiliations: [a] College of Computer Science and Technology, Jilin University, Jilin, China | [b] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Jilin, China
Correspondence: [*] Corresponding author: Hangqi Fan, College of Computer Science and Technology, Jilin University, Jilin, China. E-mail: fanhq19@mails.jlu.edu.cn.
Abstract: Bayesian network (BN) is one of the most powerful probabilistic models in the field of uncertain knowledge representation and reasoning. During the past decade, numerous approaches have been proposed to build directed acyclic graph (DAG) as the structural specification of BN. However, for most Bayesian network classifiers (BNCs) the directed edges in DAG substantially represent assertions of conditional independence rather than causal relationships although the learned joint probability distributions may fit data well, thus they cannot be applied to causal reasoning. In this paper, conditional entropy is introduced to measure causal uncertainty due to its asymmetry characteristic, and heuristic search strategy is applied to build Bayesian causal tree (BCT) by identifying significant causalities. The resulting highly scalable topology can represent causal relationship in terms of causal science, and corresponding joint probability can fit training data in terms of data science. Then ensemble learning strategy is applied to build Bayesian causal forest (BCF) with a set of BCTs, each taking different attribute as the root node to represent root cause for causality analysis. Extensive experiments performed on 32 public datasets from the UCI machine learning repository show that BCF achieves outstanding classification performance compared to state-of-the-art single-model BNCs (e.g., CFWNB), ensemble BNCs (e.g., WATAN, IWAODE, WAODE-MI and TAODE) and non-Bayesian learners (e.g., SVM, k-NN, LR).
Keywords: Bayesian networks, conditional entropy, causal relationships, Bayesian causal forest
DOI: 10.3233/IDA-216114
Journal: Intelligent Data Analysis, vol. 26, no. 5, pp. 1275-1302, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl