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Attribute selection is a technique to prune less relevant information and discover high‐quality knowledge. It is especially useful for the classification of a large database, because the preprocessing of data increases the possibility that predictor attributes given to the mining algorithm become more relevant to the class attribute. In this paper, a method to acquire the optimal attribute subset...
In order to extract class association rules more effectively when dealing with large attributes set, Genetic Network Programming (GNP) based class association rule mining with Attributes Importance has been proposed in this paper. The main difference between the proposed method and the conventional GNP-based class association rule mining is that Attributes Importance is introduced to affect the attributes...
In order to extract class association rules more effectively when dealing with large attributes set, Genetic Network Programming (GNP) based class association rule mining with Attributes Importance has been proposed in this paper. The main difference between the proposed method and the conventional GNP-based class association rule mining is that Attributes Importance is introduced to affect the attributes...
As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), a new approach named Genetic Network Programming (GNP) has been proposed in the evolutionary computation field. GNP uses multiple reusable nodes to construct directed-graph structures to represent its solutions. Recently, many research has clarified that GNP can work well in data mining area. In this paper, a novel evolutionary...
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