LEARNING ACCURATE AND UNDERSTANDABLE RULES FROM SVM CLASSIFIERS
Fei Chen · Summit (Simon Fraser University) · 2004
Despite of their impressive classification accuracy in many high dimensional applications, Support Vector Machine (SVM) classifiers are hard to understand because the definition of the separating hyperplanes typically involves a large percentage of all features.In this paper, we address the problem of understanding SVM classifiers, which has not yet been well-studied.We formulate the problem as learning models to approximate trained SVMs that are more understandable while preserve most of the SVM's classification quality.Our method learns a set of If-Then rules that are generally considered to be understandable and that allow an explicit control of their complexity to meet user-supplied requirements.The adoption of the unordered rule learning paradigm, along with exploiting the trained SVMs helps overcome the weakness of standard rule learners in high dimensional feature spaces.A pruning method is employed to maximize the accuracy of the resulting rule set for some user-specified complexity.Experiments demonstrate that the accuracy of the rule set is close to that achieved by SVMs and keeps stable even with substantial decreases of the rule complexity. To my familyMy greatest gratitude goes to my senior supervisor Dr. Martin Ester, who has been an excellent advisor and mentor.Martin has shared with me a great deal of his expertise in data mining and valuable experiences in writing, speaking and numerous other important aspects of conducting a career in research.His efficient working style and patience provides me with the ideal balance of freedom to pursue my own ideas and consistent guidance to steer me in the right direction.