Learning Bayesian Network Structure by Self-Generating Prior Information: The Two-step Clustering-based Strategy

Talk Abstract

Structure learning is a fundamental and challenging issue in dealing with Bayesian networks. In this talk we will discuss a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time efficiency of state-of-the-art algorithms in Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the within-cluster arcs being well preserved, we learn the between-cluster structure of the given network. Experimental results on benchmark data sets show that a wide range of structure learning algorithms benefit from the proposed clustering-based strategy in terms of both accuracy and efficiency.

Talk Slides: PDF

Code Link on GitHub: TSCB-strategy.

Paper Reference: Yikun Zhang, Yang Liu, Jiming Liu (2018) Learning Bayesian Network Structure by Self-Generating Prior Information: The Two-step Clustering-based Strategy In Proceedings of the Workshops of the Thirty-Second (AAAI-18) Conference on Artificial Intelligence, New Orleans, Louisiana, USA, pages 530-537 URL: https://aaai.org/ocs/index.php/WS/AAAIW18/paper/view/17111/ .