Searching for meaningful feature interactions with backward-chaining rule induction

Authors:
Doug Fisher;Mary Edgerton;Lianhong Tang;Lewis Frey;Zhihua Chen
Affiliations:
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN;Department of Pathology, Vanderbilt University Medical Center;Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center;Department of Biomedical Informatics, Vanderbilt University Medical Center;Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

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Abstract

Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We propose Backward-Chaining Rule Induction (BCRI) as a semi-supervised mechanism for biasing the search for plausible feature interactions. BCRI adds to a relatively limited tool-chest of hypothesis generation software, and it can be viewed as an alternative to purely unsupervised association rule learning. We illustrate BCRI by using it to search for gene-to-gene causal mechanisms. Mapping hypothesized gene interactions against a domain theory of prior knowledge offers support and explanations for hypothesized interactions, and suggests gaps in the current domain theory, which induction might help fill.