Active Learning for Causal Bayesian Network Structure with Non-symmetrical Entropy

Authors:
Guoliang Li;Tze-Yun Leong
Affiliations:
School of Computing, National University of Singapore, Law Link, Singapore 117590;School of Computing, National University of Singapore, Law Link, Singapore 117590
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

Citing 7
Cited 1

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
A tutorial on learning with Bayesian networks

Learning in graphical models
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
Causal Discovery from Changes

UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Active learning for structure in Bayesian networks

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Causal discovery from a mixture of experimental and observational data

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Incremental causal network construction over event streams

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in causal Bayesian networks involves interventions by manipulating specific variables, and observing the patterns of change over other variables to derive causal knowledge. In this paper, we propose a new active learning approach that supports interventions with node selection. Our method admits a node selection criterion based on non-symmetrical entropy from the current data and a stop criterion based on structure entropy of the resulting networks. We examine the technical challenges and practical issues involved. Experimental results on a set of benchmark Bayesian networks are promising. The proposed method is potentially useful in many real-life applications where multiple instances are collected as a data set in each active learning step.