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Association rule-based classification is one of the most important data mining techniques applied to many scientific problems. In the last few years, extensive research has been carried out to develop enhanced methods and obtained higher classification accuracies than traditional classifiers. However, the current studies show that the association rule-based classifiers may also suffer some problems...
Classification rule mining is a practical data mining technique widely used in real world. In the previous work, we have put forward a fuzzy class association rule mining method based on genetic network programming and applied it to network intrusion detection system which proved its efficiency and advantage. In this paper, a detailed comparison not only between fuzzy class association rule minings(FCARMs)...
In this paper, a method of time-related class association rule mining is proposed based on genetic network programming (GNP) combined with estimation of distribution algorithms (EDAs). There are two important points in this paper: the first important point is to combine GNP with estimation of distribution algorithms which are a novel evolution strategy. The second important point is that three kinds...
In this paper, a novel evolutionary paradigm combining Genetic Network Programming (GNP) and Estimation of Distribution Algorithms (EDAs) is proposed and used to find important association rules in time-related applications, especially in traffic prediction. GNP is one of the evolutionary optimization algorithms, which uses directed-graph structures. EDAs is a novel algorithm, where the new population...
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