| Summary: | 碩士 === 國立中央大學 === 資訊管理學系 === 101 === The rapid development of information system has led to increase a large number of data, and these data usually imply valuable information, for which machine learning and data mining are useful tools to extract knowledge and rules from data. Classification is one of important issues, and it is important to construct a good classifier that can classify the data correctly. Although some famous classifiers had been presented, such as support vector machine (SVM), artificial neuro network (ANN), and K-nearest neighbors (KNN), they lack of being understood by people. Therefore, in this study, to classification problems, we propose a metacognitive complex fuzzy cognitive map (McCFCM) that combines metacognitive, complex fuzzy sets and fuzzy cognitive map. The modeling of McCFCM classifier comprises the phases of parameter learning and structure learning. In the parameter learning phase, the method of standard particle swarm optimization (SPSO) is use to adjust the location of the complex fuzzy sets and the weights of all the connections in FCM; In the structure learning phase, the algorithm of binary particle swarm optimization (BPSO) is used to establish or erase some connections in FCM. In order to build a more robust classifier, we also use one-against-all (OAA) to decompose the dataset whose data are with multiple classes into several binary-class subsets, and the Fisher score (F-score) is used to pick important features for the problem of classification. In this study, ten datasets from the University of California-Irvine (UCI) machine learning repository have been used to evaluate the performance by the proposed McCFCM classifier, whose results are compared with those by other noted classification algorithms.
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