Backward chaining rule induction

Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe Backward-Chaining Rule Induction (BCRI) as a semi-supervised mechanism for biasing the search for IF-THEN rules that express plausible feature interactions. BCRI adds to a relatively limited tool-chest of hypothesis generation software and is an alternative to purely unsupervised association-rule learning. We illustrate BCRI by using it to search for gene-to-gene causal mechanisms that underlie lung cancer. Mapping hypothesized gene interactions against prior knowledge offers support and explanations for hypothesized interactions, and suggests gaps in current knowledge that induction might help fill. BCRI is implemented as a wrapper around a base supervised-rule-learning algorithm. We summarize our prior work with an adaptation of C4.5 as the base algorithm (C45-BCRI), extending this in the current study to use Brute as the base algorithm (Brute...

• Type: Journal
• Year: 2006
• Where: IDA
• Authors: Douglas H. Fisher, Mary E. Edgerton, Zhihua Chen, Lianhong Tang, Lewis Frey
• Keywords: Base Algorithm, Gene Expression Microarray, IDA 2006, Information Technology, Possible Feature Interactions