| Summary: | 碩士 === 國立清華大學 === 工業工程與工程管理學系 === 100 === In the field of data mining, classification is one of the most discussed issues that generating a generalized known structure to apply to new data. Recently, support vector machine (SVM) has been introduced for analyzing data and recognizing patterns. It’s a useful technique for data classification and regression analysis. However, while using SVM dealing with each unique classification problem; it is not known beforehand which parameter combination is the best for a given problem. Users often need to do random self-test or apply other algorithm to find an acceptable solution. In this study, we proposed a support vector machine classification combined with swarm intelligence algorithm, called Support Vector Machine based on Simplified Swarm Optimization (SSO-SVM). The simplified swarm optimization (SSO) is an emerging population-based stochastic optimization method, which belongs to both categories of swarm intelligence and evolutionary computation. In this paper, simplified swarm optimization (SSO) is used to implement a parameter combination selection, and support vector machine (SVM) serve as a fitness function of SSO for classification problem. The result indicates that the proposed SSO-SVM has better performance and more efficient than other method listed in this paper.
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