Approximate match of rules using backpropagation neural networks

作     者：Kijsirikul, B Sinthupinyo, S Chongkasemwongse, K 

作者机构：Chulalongkorn Univ Dept Comp Engn Bangkok 10330 Thailand 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：2001年第44卷第3期

页      面：273-299页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Thailand Research Fund  TRF 

主　　题：approximate match feature generation inductive logic programming backpropagation neural networks 

摘      要：This paper presents a method for approximate match of first-order rules with unseen data. The method is useful especially in case of a multi-class problem or a noisy domain where unseen data are often not covered by the rules. Our method employs the Backpropagation Neural Network for the approximation. To build the network, we propose a technique for generating features from the rules to be used as inputs to the network. Our method has been evaluated on four domains of first-order learning problems. The experimental results show improvements of our method over the use of the original rules. We also applied our method to approximate match of propositional rules converted from an unpruned decision tree. In this case, our method can be thought of as soft-pruning of the decision tree. The results on multi-class learning domains in the UCI repository of machine learning databases show that our method performs better than standard C4.5 s pruned and unpruned trees.