doi: 10.7763/IJIET.2011.V1.44
Two New Heuristic Methods Based on Crisp and Fuzzy Partitions for Training Data Reduction
Tri Minh Huynh*
Abstract
This paper is to introduce two heuristic methods based on crisp and fuzzy partitions for selecting the subset of instances from the training data set in high dimensional problems. This subset is called the representative training data set (RTR). A proposed genetic algorithm (GA) is used to learn a compact fuzzy rule-based system (FRBS) with the instances of RTR. RTR size is rather smaller than the initial training data set, thus time cost for learning FRBS decreases significantly. Therein the number of fuzzy rules is not only reduced but rule lengths are also shorter. The smaller size of the rule base is closely related to the interpretability of the FRBS. As a result, the final FBRS gets a suitable and acceptable balance between interpretability and accuracy.
Keywords
- Crisp partition
- fuzzy partition
- fuzzy rule set reduction
- data reduction techniques
- genetic algorithm
- interpretability
How to Cite
Tri Minh Huynh, "Two New Heuristic Methods Based on Crisp and Fuzzy Partitions for Training Data Reduction," International Journal of Information and Education Technology, vol. 1, no. 4, pp. 273-279, 2011. https://doi.org/10.7763/IJIET.2011.V1.44
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).