Abstract
Recently, the development of machine-learning techniques has provided an effective analysis tool for classification problems. The support vector machine (SVM) is one of the most popular supervised learning techniques. However, the SVM may not effectively detect the instance of the minority class and obtain lower classification performance in the overlap region when learning from complicated data sets. The complicated data sets with class imbalanced and overlapped distributions are common in most practical applications. Moreover, they negatively affect the classification performances of the SVM. For predicting the rarest objects, if the training instances of the majority class outnumber than the other minority class, the hyperplane or decision boundary generated by SVM can be severely skewed toward the majority class, especially in the class imbalanced or overlapping data sets. Hence, this study aims to develop the robust SVM to enhance their classification performance. Moreover, another challenge is that SVM are regarded as black box analysis tools lacking of explanation capability in the classification problem. Decision boundary of SVM always lacks explicit a declarative knowledge representation since it presents a complicated mathematical pattern. Therefore, a supportive rule extraction algorithm from SVM is needed This study aims to develop three models, that are “modified slack variables within SVM” (MS-SVM), “Genetic Algorithm based Rule Extraction Algorithm from SVM” (GASVM), and “Integration of Kernel Clustering with Genetic Algorithm based Rule Extraction Algorithm from SVM” (KCGex-SVM). The first proposed method, MS-SVM, is applied to deal with complex data. The artificial and UCI data sets are provided to evaluate to the effectiveness of MS-SVM model. By using different performance metrics, accuracy, sensitivity, and specificity, the experimental results can be compared with the original SVM demonstrating the superiority of the MS-SVM. The second and the third ones are GASVM and KCGex-SVM proposed to enhance the explanation capability of SVM and extract the rule sets. This study utilizes measurements of accuracy, coverage, fidelity, and comprehensibility to evaluate the performance of this proposed rule extraction algorithms on the UCI data sets. Results indicate that the performance of the proposed rule extraction algorithms is better than that of others Thus, the proposed rule extraction algorithms are essential analysis tools which can be effectively used in data mining fields. A real application is introduced in this study as well. The actual medical pressure ulcer after surgical operation is employed to illustrate the superiority of our proposed MS-SVM and GASVM. The results of the real application demonstrate that our proposed methods are practical for the real world case.