Rule Extraction from SVM for Protein Structure Prediction

Abstract

In recent years, many researches have focused on improving the accuracy of protein structure prediction, and many significant results have been achieved. However, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of a decision is important for the acceptance of machine learning technology in bioinformatics applications such as protein structure prediction. The support vector machines (SVMs) have shown better performance than most traditional machine learning approaches in a variety of application areas. However, the SVMs are still black box models. They do not produce comprehensible models that account for the predictions they make. To overcome this limitation, in this chapter, we present two new approaches of rule generation for understanding protein structure prediction. Based on the strong generalization ability of the SVM and the interpretation of the decision tree, one approach combines SVMs with decision trees into a new algorithm called SVM_DT. Another method combines SVMs with association rule (AR) based scheme called SVM_PCPAR. We also provide the method of rule aggregation for a large number of rules to produce the super rules by using conceptual clustering. The results of the experiments for protein structure prediction show that not only the comprehensibility of SVM_DT and SVM_PCPAR are much better than that of SVMs, but also that the test accuracy of these rules is comparable. We believe that SVM_DT and SVM_PCPAR can be used for protein structure prediction, and understanding the prediction as well. The prediction and its interpretation can be used for guiding biological experiments.