| Summary: | 碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 100 === DNA microarray can be used to analyze the specified data efficiently and effectively so as provide the observations of gene expression differences and their changes among genes. In recent years, some studies in literature have pointed out that mutation of a single gene often associated with concurrent disease so that how to find the key of genes combination from DNA sequence becomes a very important issue. Cancers are still the top ten causes of death in past decades, how to diagnosis the cancer in very early occurrence has been a major plan of the medical field. The aim of this study is to investigate how to find the best genes combination and relationship strength for identifying the categories of cancers from the microarray data. In this paper, a hybrid meta-evolutionary approach with grid computing architecture to assess microarray data pattern in the cancer classification problems is proposed for extracting the fuzzy cognitive map including the predictors, the corresponding parameters of the map simultaneously so as to building a decision making model with maximum classification accuracy. Without experts’ attendance to construct the fuzzy cognitive map, it can be developed in the proposed approach based on the numerical data to do decision analytic, it is bound to bring a new breakthrough in FCM applications. Through the numerical experiments, we compared our results against the methods in literature and the commercial data mining software, and then we show experimentally that the proposed approach is promising for improving prediction accuracy and enhancing the modeling simplicity.
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