Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 114, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2022.105212
Keywords
Bayesian networks; Causality; Probabilistic graphical models; Bayesian learning
Categories
Funding
- National Key Research and Development Program of China [2019YFC1804804]
- Scientific and Technological Devel-oping Scheme of Jilin Province, China [20200201281JC]
- High Performance Computing Center of Jilin University, China
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This paper introduces a novel algorithm for measuring, describing and evaluating causal relationships in Bayesian network learning framework, and demonstrates competitive classification performance and excellent causal interpretation compared to state-of-the-art Bayesian network classifiers on multiple datasets through a series of experiments.
Revealing causal information by analyzing purely observational data, known as causal discovery, has drawn much attention. To prove that the causal knowledge mined from data can be applied to facilitate various machine learning tasks (e.g., classification), we propose to measure, describe and evaluate the causalities in the framework of Bayesian network (BN) learning. In this paper, heuristic search strategy is applied to explore the causal interpretation in the form of directed acyclic graph (DAG) for classification. While adding directed edges to the DAG, we first introduce the log-likelihood equivalence assertion to make the learned joint probability encoded in BN approximates the true one, then introduce the causal dependence assertion to assess the rationality of the learned causal relationship. We perform a range of experiments on 35 datasets and empirically show that this novel algorithm demonstrates competitive classification performance and excellent causal interpretation compared to state-of-the-art Bayesian network classifiers (e.g. SKDB, WATAN, SLB, and TAODE).
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