Rule extraction from support vector machines by genetic algorithms

Support vector machines (SVMs) are state-of-the-art tools used to address issues pertinent to classification. However, the explanation capabilities of SVMs are also their main weakness, which is why SVMs are typically regarded as incomprehensible black box models. In the present study, a rule extraction algorithm to extract the comprehensible rule from SVMs and enhance their explanation capability is proposed. The proposed algorithm seeks to use the support vectors from a training model of SVMs and combine genetic algorithms for constructing rule sets. The proposed method can not only generate rule sets from SVMs based on the mixed discrete and continuous variables but can also select important variables in the rule set simultaneously. Measurements of accuracy, sensitivity, specificity, and fidelity are utilized to compare the performance of the proposed method with direct learner algorithms and several rule-extraction techniques from SVMs. The results indicate that the proposed method performs at least as well as with the most successful direct rule learners. Finally, an actual case of pressure ulcer was studied, and the results indicated the practicality of our proposed method in real applications.