Fuzzy classification rule mining based on Genetic Network Programming algorithm

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 inherited from association rule mining such as handling of (1) continuous data and (2) the support/confidence framework. In this paper, a novel fuzzy classification model based on genetic network programming (GNP) that can deal with the above problems has been proposed. GNP is one of the evolutionary optimization algorithms that uses directed graph structures as solutions instead of strings (genetic algorithms) or trees (genetic programming). Therefore, GNP can deal with more complex problems by using the higher expression ability of graph structures. The performance of our algorithm has been compared with other relevant algorithms and the experimental results show the advantages and effectiveness of the proposed model.