Browse > Article
http://dx.doi.org/10.5391/JKIIS.2008.18.5.685

Extraction of Classification Boundary for Fuzzy Partitions and Its Application to Pattern Classification  

Son, Chang-S. (영남대학교 전기공학과)
Seo, Suk-T. (영남대학교 전기공학과)
Chung, Hwan-M. (대구가톨릭대학교 컴퓨터정보통신공학부)
Kwon, Soon-H. (영남대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.5, 2008 , pp. 685-691 More about this Journal
Abstract
The selection of classification boundaries in fuzzy rule- based classification systems is an important and difficult problem. So various methods based on learning processes such as neural network, genetic algorithm, and so on have been proposed for it. In a previous study, we pointed out the limitation of the methods and discussed a method for fuzzy partitioning in the overlapped region on feature space in order to overcome the time-consuming when the additional parameters for tuning fuzzy membership functions are necessary. In this paper, we propose a method to determine three types of classification boundaries(i.e., non-overlapping, overlapping, and a boundary point) on the basis of statistical information of the given dataset without learning by extending the method described in the study. Finally, we show the effectiveness of the proposed method through experimental results applied to pattern classification problems using the modified IRIS and standard IRIS datasets.
Keywords
pattern classification; classification boundaries; fuzzy partition; rule reduction;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 A. Skowron and C. M. Rauszer, "The discernibility matrices and functions in information systems," Institute of computer sciences report 1/91, Technical University of Warsaw, pp. 1-41, 1991
2 H. Ishibuchi, T. Nakashima, and T. Murata, "Performance of fuzzy classifier systems for multidimensional pattern classification problems," IEEE Transactions on SMC, Part B: Cybernetics, vol. 29, no. 5, pp. 601-618, 1999
3 J. R. Quinlan, C4.5: Programs for machine learning, Morgan Kaufman, 1993
4 UCI Repository of Machine Learning Databases, Depatment of Information and Computer Science, University of California, Irvine, CA, Available: http://mlearn.ics.uci.edu/MLRepository.html
5 H. Ishibuchi, T. Murata, and I. B. Turksen, "Single objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems," Fuzzy sets and systems, vol. 89, no. 2, pp. 135-150, 1997   DOI   ScienceOn
6 손창식, 정환묵, 서석태, 권순학, "규칙의 커플링문제를 최소화하기 위한 퍼지-러프 분류방법," 한국퍼지 및 지능시스템 학회 논문지, 제17권, 4호, pp. 460-465, 2007   과학기술학회마을   DOI
7 A. Gonzalez and R. Perez, "Selection of relevant features in a fuzzy genetic learning algorithm," IEEE Transactions on SMC - Part B: Cybernetics, vol. 31, no. 3, pp. 417-425, 2001
8 D. Nauck, U. Nauck, and R. Kruse, "Generating classification rules with the neuro-fuzzy system NEFCLASS," In Proc. the biennial conference of NAFIPS, Berkeley, pp. 19-22, 1996
9 H. Ishibuchi, T. Nakashima, "Effect of rule weights in fuzzy rule-based classification systems," IEEE Transactions on Fuzzy Systems, vol. 9, no. 4, pp. 506-515, 2001   DOI   ScienceOn
10 H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka, "Selecting fuzzy if-then rules for classification problems using genetic algorithms," IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, pp. 260-269, 1995   DOI   ScienceOn
11 N. Garcia-Pedrajas, C. Garcia-Osorio, and C. Fyfe, "Nonlinear boosting projections for ensemble construction," Journal of Machine Learning Research, vol. 8, pp. 1-33, 2007
12 J-S. R. Jang, "ANFIS : Adaptive network based fuzzy inference systems," IEEE Transactions on SMC, vol. 23, no. 3, pp. 665-695, 1993   DOI   ScienceOn
13 손창식, 정환묵, 권순학, "퍼지 규칙기반 분류시스템에서 퍼지 분할의 선택방법," 한국지능시스템학회 논문지, 제18권, 3호, pp. 360-366, 2008   과학기술학회마을   DOI