Publication Type

Conference Proceeding Article

Publication Date

7-2009

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. © Springer-Verlag Berlin Heidelberg 2009.

Keywords

Active learning, Bayesian networks, Intervention, Node selection, Non-symmetrical entropy, Stop criterion

Discipline

Databases and Information Systems

Research Areas

Intelligent Systems and Decision Analytics

Publication

PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining

First Page

290

Last Page

301

ISBN

9783642013065

Identifier

10.1007/978-3-642-01307-2_28

Publisher

Springer-Verlag

City or Country

Bangkok,Thailand

Share

COinS