Multi-class Joint Rule Extraction and Feature Selection for Biological Data

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Joint Rule Extraction and Feature Selection (JRF), based on 1-norm regularized random forests. JRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied JRF to several drug activity prediction and microarray data sets. JRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.