Approach to Clustering with Variance-Based XCS
- 20 September 2017
- journal article
- Published by Fuji Technology Press Ltd. in Journal of Advanced Computational Intelligence and Intelligent Informatics
- Vol. 21 (5), 885-894
- https://doi.org/10.20965/jaciii.2017.p0885
Abstract
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 bench-mark problem to examine whether XCS-VRc can cluster both the generalized and more generalized features in the input data. The proposed XCS-VRc proved to be more efficient than XCS and the conventional XCS-VR.Keywords
This publication has 6 references indexed in Scilit:
- Learning classifier systems from a reinforcement learning perspectiveSoft Computing, 2002
- Anticipations Control Behavior: Animal Behavior in an Anticipatory Learning Classifier SystemAdaptive Behavior, 2002
- Classifier Fitness Based on AccuracyEvolutionary Computation, 1995
- A training algorithm for optimal margin classifiersPublished by Association for Computing Machinery (ACM) ,1992
- Learning to predict by the methods of temporal differencesMachine Learning, 1988
- Induction of decision treesMachine Learning, 1986