Mining Classification Rules in Multistrategy Learning Approach

Article type: Research Article

Authors: Yuanhui, Zhou^{; *} | Yuchang, Lu | Chunyi, Shi

Affiliations: The National Key Laboratory of Intelligence Technology and System, Department of Computer Science, Tsinghua University, Beijing, 100, China

Correspondence: [*] Corresponding author.

Abstract: Classification, which involves finding rules that partition a given dataset into disjoint groups, is one class of data mining problems. Approaches proposed so far for mining classification rules from databases are mainly decision tree based on symbolic learning methods. In this paper, we combine artificial neural network and genetic algorithm to mine 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.

Keywords: Data mining, Machine learning

DOI: 10.3233/IDA-1998-2302

Journal: Intelligent Data Analysis, vol. 2, no. 3, pp. 165-185, 1998