Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

A Weighted FMM Neural Network and Feature Analysis Technique for Pattern Classification

가중치를 갖는 FMM신경망과 패턴분류를 위한 특징분석 기법

  • 김호준 (한동대학교 전산전자공학부) ;
  • 양현승 (한국과학기술원 전산학과)
  • Published : 2005.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.

본 논문에서는 패턴 분류를 위한 수정된 퍼지 최대최소 신경망 모델을 제안하고 그의 유용성을 고찰한다. 이를 위하여 하이퍼박스 내에서 각 특징들에 대하여 가중치 요소론 갖는 새로운 하이퍼큐브 소속함수를 정의한다. 이 가중치 요소는 분류과정에서 임의의 클래스에 대한 각 특징의 상대적인 기여도를 반영한다. 본 연구에서는 이를 위하여 새롭게 정의된 하이퍼박스 생성, 확장 및 축소의 3단계로 이루어지는 학습 방법론을 소개한다. 또한 제안된 모델을 기반으로 하여 학습된 분류기로부터 하이퍼박스 소속함수와 연결가중치를 사용하여 주어진 클래스에 대한 특징의 연관도를 산출하는 형태의 이른바 특징 분석 기법을 제안한다. 이를 위하여 세부적으로 각 특징에 대하여 연관도 척도와 퍼지 소속함수간의 유사도 척도를 정의한다. 또한 실제 패턴 분류문제에 적용한 실험결과를 통하여 제안된 이론의 타당성을 평가한다.

Keywords

References

  1. S. Mitra, and Y. Hayashi, 'Neuro-Fuzzy Rule Generation: Survey in Soft Computing Framework,' IEEE Transactions on Neural Networks, Vol.11, No.3, pp.748-768, 2000 https://doi.org/10.1109/72.846746
  2. P. K. Simpson, 'Fuzzy Min-Max Neural Networks-Part 1:Classification,' IEEE Transaction on Neural Networks, Vol.3, No.5, pp.776-786, 1992 https://doi.org/10.1109/72.159066
  3. ？S. Mitra, R. K. De, and S. K. Pal, 'Knowledge- Based Fuzzy MLP for Classification and Rule Generation,' IEEE Transactions on Neural Networks, Vol.8, No.6, pp.1338-1350, 1997 https://doi.org/10.1109/72.641457
  4. ？B. Gabrys, A. Bargiela, 'General Fuzzy Min-Max Neural Network for Clustering and Classification,' IEEE Transaction on Neural Networks, Vol.11, No.3, 2000 https://doi.org/10.1109/72.846747
  5. P. K. Simpson, 'Fuzzy Min-Max Neural Networks Part 2:Clustering,' IEEE Transaction on Fuzzy Systems, Vol.1, No.1, pp.32-45, 1993 https://doi.org/10.1109/TFUZZ.1993.390282
  6. 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, pp.145-152, 2002 https://doi.org/10.1007/BF02348118
  7. Raphael Feraud, Fabrice Clerot, 'A Methodology to Explain Neural Network Classification,' Neural Networks, Vol.15, No.1, pp.237-246, 2002 https://doi.org/10.1016/S0893-6080(01)00127-7
  8. Kumar S. Ray and Jayati Ghoshal, 'Neuro Fuzzy Approach to Pattern Recognition,' Neural Networks, Vol.10, No.1, pp.161-182, 1997 https://doi.org/10.1016/S0893-6080(96)00056-1
  9. Mahn M. Lee, Kuo H. Chen, I. F. Jiang, 'A Neural networks Classifier with Disjunctive Fuzzy Information,' Neural Networks, Vol.11, No.1, pp.1113-1125, 1998 https://doi.org/10.1016/S0893-6080(98)00058-6
  10. Hung P. Chiu, Din C. Tseng, 'Invariant Hand-written Chinies Character Recognition using Fuzzy Min-Max Neural Networks,' Pattern Recognition Letters, Vol.18, No.1, pp.481-491, 1997 https://doi.org/10.1016/S0167-8655(97)00029-9
  11. Jayanta Basak, Rajat K. De, Sankar K. Pal, 'Unsupervised Feature Selection using a Neuro-Fuzzy Approach,' Pattern Recognition Letters, Vol.19, No.1, pp.997-1006, 1998 https://doi.org/10.1016/S0167-8655(98)00083-X
  12. F. Behloul, B.P.E. Lelieveldt, A. Boudraa, J.H.C. Reiber, 'Optimal Design of Radial Basis Function Neural Networks for Fuzzy-Rule Extraction in High Dimensional Data,' Pattern Recognition, Vo.35, No.1, pp.659-675, 2002 https://doi.org/10.1016/S0031-3203(01)00033-4
  13. C. L. Blake, and C. J. Merz, 'UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html],' Irvine, CA: University of California, Department of Information and Computer Science, 1998