Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Extracting Minimized Feature Input And Fuzzy Rules Using A Fuzzy Neural Network And Non-Overlap Area Distribution Measurement Method

퍼지신경망과 비중복면적 분산 측정법을 이용한 최소의 특징입력 및 퍼지규칙의 추출

  • Lim Joon-Shik (Department of E-Commerce Software, Kyungwon University)
  • 임준식 (경원대학교 전자거래학부)
  • Published : 2005.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NEWFM)을 이용하여 위스콘신 유방암(Wisconsin breast cancer)의 진단을 수행하는 퍼지규칙을 추출하고, 비중복면적 분산 측정법을 사용하여 특징입력수를 최소로하는 방안을 제안하고 있다. NEWFM 구조의 중간 부분인 하이퍼박스(hyperbox)들은n 개의 대, 중, 소로 구성된 가중 퍼지소속함수 집합으로 구성되며, 학습 후 각 집합의 대, 중, 소로 구성된 가중 퍼지소속함수는 퍼지집합의 경계합(bounded sum)을 사용하여 다시 하나의 가중 퍼지소속함수로 합성(BSWFM) 된다. n 개의 특징입력(feature input)은 학습된 모든 하이퍼박스에 연결되어 예측 작업을 수행한다. 여기에 비중복면적 분산 측정법을 적용하여 중요도가 낮은 특징입력을 제거하면서 최소의 m 개 특징입력만을 사용한 하이퍼박스로 단순화시킨다. 이러한 방법으로 위스콘신 유방암의 9개의 특징입력 중 4개를 사용하여 NEWFM으로 추출된 2개의 퍼지규칙은 99.71%의 예측 인식율을 가지며 이는 퍼지규칙의 수와 인식율에 있어 현재 발표된 논문의 결과보다 우수함을 보여준다.

Keywords

References

  1. G. A. Carpenter, S. Grossberg and J. Reynolds, 'ARTMAP: Supervised real-time learning and classification of non stationary data by a self-organizing neural network,' Neural Networks, Vol. 4, pp.565-588, 1991 https://doi.org/10.1016/0893-6080(91)90012-T
  2. A. F. Gomez -Skarmeta, M. V. F. Jimenez, J. G. Marin -Blazques, 'Approximative Fuzzy Rules Approaches for Classification with Hybrid-Gs, Techniques,' Informaion Sciences, Vol. 136, pp.193-214, 2001 https://doi.org/10.1016/S0020-0255(01)00148-7
  3. H. Ishibuchi and T. Nakashima, 'Voting in Fuzzy Rule-Based Systems for Pattern Classification Problems,' Fuzzy Sets and Systems, Vol. 103, pp.223-238, 1999 https://doi.org/10.1016/S0165-0114(98)00223-1
  4. R. Jang, 'ANFIS: Adaptive network-based fuzzy inference system,' IEEE Trans. Syst., Man, Cybern., Vol. 23, pp.665- 685, May-June, 1993 https://doi.org/10.1109/21.256541
  5. C. F. Juang and C. T. Lin, 'An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications,' IEEE Trans. Fuzzy Systems, Vol. 6, No.1, pp.12-32, 1998 https://doi.org/10.1109/91.660805
  6. N. Kasabov, Foundation of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, Cambridge, MA, 1996
  7. T. Kasuba, 'Simplified Fuzzy ARTMAP,' IEEE AI Expert, pp.19-25, Nov., 1993
  8. Ho J. Kim, Tae W. Ryu, Thai T. Nguyen, Ioon S. Lim, and Sudhir Gupta, 'A Modified Fuzzy Min-Max Neural Network for Pattern Classification,' Computational Science and Its Applications ICCSA 2004 (LNCS 3046), pp.792-798, Springer-Verlag, 2004
  9. H.-M. Lee, K.-H. Chen and I-F. Jiang, 'A Neural Networks with Disjunctive Fuzzy Information,' Neural Networks, Vol. 11, pp. 1113-1125, 1998 https://doi.org/10.1016/S0893-6080(98)00058-6
  10. Joon Shik Lim, 'Finding Fuzzy Rules by Neural Network with Weighted Fuzzy Membership Function,' International Journal of Fuzzy Logic and Intelligent Systems, Vol. 4, No.2, pp.211-216, September, 2004
  11. C. T. Lin and C. S. George Lee, 'Neural-network-based fuzzy logic control and decision systern,' IEEE Trans. Computers, Vol. 40, No.12, Dec., 1991
  12. D. Nauck and R. Kruse, 'A Neuro-Fuzzy Method to Learn Fuzzy Classification Rules from Data,' Fuzzy Sets and Systems, Vol. 89, pp.277-288, 1997 https://doi.org/10.1016/S0165-0114(97)00009-2
  13. M. Setnes and H. Roubos, 'GA-Fuzzy Modeling and Classification : Complexity and Performance,' IEEE Trans., Fuzzy Systems, Vol.8, No.5, pp.509-522, 2000 https://doi.org/10.1109/91.873575
  14. P. Simpson, 'Fuzzy min-max neural networks-Part 1: Classification,' IEEE Trans., Neural Networks, Vol.3, pp. 776-786, 1992 https://doi.org/10.1109/72.159066
  15. K. Tanaka, M. Sano and H. Watanabe, 'Modeling and Control of Carbon Monoxide Concentration Using a Neuro-Fuzzy technique,' IEEE Trans., Fuzzy Systems, Vol.3, pp.271-279, June, 1995 https://doi.org/10.1109/91.413233
  16. C. Z. Ye, J. Yang, D. Y. Geng, y. Zhou, N. Y. Chen, Fuzzy Rules to Predict Degree of Malignancy in Brain Glioma, Medical and Biological Engineering and Computing, Vol.40, 2002
  17. S. Wang and C. S. G. Lee, 'Self-Adaptive Neuro-Fuzzy Inference System for Classification Applications,' IEEE Trans., Fuzzy Systems, Vol. 10, No.6, pp.790-802, 2002 https://doi.org/10.1109/TFUZZ.2002.805880
  18. W. Wolberg, O. Mangasarian, 'Multi surface Method of Pattern for Medical Diagnosis Applied to Breast Cytology,' Prac. National Academy of Sciences, Vol.87, pp.9193-9166, 1990 https://doi.org/10.1073/pnas.87.23.9193