Combining neural network, genetic algorithm and symbolic learning approach to discover knowledge from databases
Zhou Yuanhui, Lu Yuchang, Shi Chunyi · 2002
Classification, which involves finding rules that partition a given data set into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules for databases are mainly decision tree based on symbolic learning methods. In this paper, we combine artificial neural network, genetic algorithm and symbol learning methods to find classification rules. Some experiments have demonstrated that our method generates rules of better performance than the decision tree approach and the number of extracted rules is fewer than that of C4.5.