Approach to clustering with variance-based XCS

被引：0
作者：
Zhang C. [1 ]
Tatsumi T. [1 ]
Nakata M. [2 ]
Takadama K. [1 ]
机构：
[1] University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, Tokyo
[2] Yokohama National University, 79-1 Tokiwadai, Hodogaya-ku, Yokohama
来源：
| 1600年 / Fuji Technology Press卷 / 21期
基金：
日本学术振兴会;
关键词：
Learning classifier system; Machine learning;
D O I：
10.20965/jaciii.2017.p0885
中图分类号：
学科分类号：
摘要：
This paper presents an approach to clustering that extends the variance-based Learning Classifier System (XCS-VR). In real world problems, the ability to combine similar rules is crucial in the knowledge discovery and data mining field. Conventionally, XCS-VR is able to acquire generalized rules, but it cannot further acquire more generalized rules from these rules. The proposed approach (called XCS-VRc) accomplishes this by integrating similar generalized rules. To validate the proposed approach, we designed a benchmark problem to examine whether XCS-VRc can cluster both the generalized andmore generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCSVR.
引用
收藏
页码：885 / 894
页数：9
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