標題: Fuzzy inductive learning strategies
作者: Wang, CH
Tsai, CR
Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: AQR algorithm;fuzzy classification;fuzzy inductive learning;machine learning;soft instances
公開日期: 1-Mar-2003
摘要: In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from "soft" instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.
URI: http://dx.doi.org/10.1023/A:1021938425987
http://hdl.handle.net/11536/28082
ISSN: 0924-669X
DOI: 10.1023/A:1021938425987
期刊: APPLIED INTELLIGENCE
Volume: 18
Issue: 2
起始頁: 179
結束頁: 193
Appears in Collections:Articles