A Genetic Algorithm Based Fuzzy Inference System for Pattern Classification and Rule Extraction

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Setting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity.


  • Keywords


    Fuzzy Inference System; Genetic Algorithm;, Pattern Classification,; Rule Extraction

  • References


      [1] R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification”, 2nd edition, Wiley, New York, 2001.

      [2] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol 521, pp. 436-441, 2015.

      [3] D. Cui, G. Zhang, K. Hu, W. Han, and G. Huang, “Face recognition using total loss function on face database with ID photos,” Opt. Laser Technol., 2017.

      [4] S. Bijarnia, “Pyramid Binary Pattern for Age Invariant Face Verification,” 13th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) , pp. 13–16, 2017.

      [5] W. Ouyang, X. Zeng, S. Member, and X. Wang, “DeepID-Net : Object Detection with Deformable Part Based Convolutional Neural Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 7, pp. 1320–1334, 2017.

      [6] K. He and J. Sun, “Deep Residual Learning for Image Recognition,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 1–9, 2016.

      [7] Z. Bai, L. Lekamalage, C. Kasun, and G. Huang, “Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine,” Procedia - Procedia Comput. Sci., vol. 53, pp. 391–399, 2015.

      [8] S. Chen, and G. Jiang, “The Prediction Model of Multiple Myeloma Based on the BP Artificial Neural Network”, in Proc. of the International Conference on Technology and Applications in Biomedicine, pp. 380-382, 2008.

      [9] J.M. Benitez, J.L. Castro and I.Requena, “Are Artificial Neural Networks Black Boxes?” IEEE Trans. Neural Netw., vol 8, no.5, pp. 1156-1164, Sep 1997.

      [10] E. Kolman and M. Margaliot, “Are Artificial Neural Networks White Boxes?” IEEE Trans. Neural Netw., vol 16, no.4, pp. 844-852, Jul. 2005.

      [11] K.S. Yap, C.P. Lim and J.M. Salleh, “An enhanced generalized adaptive resonance theory network and its application to medical pattern classification”, Journal of Intelligent & Fuzzy Systems 21, pp. 65-78, 2010.

      [12] J D J Rubio, “USNFIS: Uniform Stable Neuro Fuzzy Inference System,” Neurocomputing, vol. 262, pp. 57-66, 2017.

      [13] Jun H Chung , JM Pak , CK Ahn , SH You , MT Lim , MK Song, “Particle filtering approach to membership function adjustment in fuzzy logic systems,”, Neurocomputing, no. 237, pp.166–174, 2017.

      [14] Zadeh LA, “Outline of a new approach to the analysis of complex systems and decision processes”, In: yager RR, Ovchinnikov S., Tong RM., Nguyen HT., editors. IEEE Trans. Systems, Man and Cybernetics. SMC-3, Fuzzy sets and applications, pp. 28-44, 1973.

      [15] CM Lin, TL Le, TT Huynh, “Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control,” Neurocomputing, vol. 275, pp. 2239-2250, 2018.

      [16] S. Y. Wong, K. S. Yap, H. J. Yap, and S. C. Tan, “A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification,” Neural Process. Lett., vol. 42, no. 3, pp. 585–602, 2015.

      [17] S. Y. Wong, K. S. Yap, and H. J. Yap, “A Constrained Optimization based Extreme Learning Machine for noisy data regression,” Neurocomputing, vol. 171, pp. 1431–1443, 2016.

      [18] S. Y. Wong, K. S. Yap, H. J. Yap, S. C. Tan, and S. W. Chang, “On equivalence of FIS and ELM for interpretable rule-based knowledge representation,” IEEE Trans. Neural Networks Learn. Syst., vol. 26, no. 7, pp. 1417–1430, 2015.

      [19] S. Y. Wong, K. S. Yap, and H. J. Yap, “Constrained-Optimization based Bayesian Posterior Probability Extreme Learning Machine for pattern classification,” International Conference on Neural Information Processing, Springer, 2014.

      [20] ST Wang, H Ishibuchi, Z Bian, “Joint Learning of Spectral Clustering Structure and Fuzzy Similarity Matrix of Data, IEEE Transactions on Fuzzy Systems, 2018

      [21] Y Zhang, H Ishibuchi, S Wang, “Deep Takagi–Sugeno–Kang Fuzzy Classifier With Shared Linguistic Fuzzy Rules,” IEEE Transactions on Fuzzy Systems, 2018.

      [22] MC Pablo, C Morillas, Hans E. Plesser , S. Romeroa , F. Pelayo, “Genetic algorithm for optimization of models of the early stages in the visual system,” Neurocomputing, no. 250, pp. 101–108, 2017.

      [23] Younas, F. Kamrani, M. Bashir, J. Schubert, “Efficient genetic algorithms for optimal assignment of tasks to teams of agents,” Neurocomputing, pp.1–20, 2018.

      [24] B. Haznedar, A. Kalinli, “Training ANFIS structure using simulated annealing algorithm for dynamic systems identification,”, Neurocomputing, vol. 302, pp. 66-74, 2018.

      [25] Huai-xiang Zhang, Bo Zhang, Feng Wang, “ Automatic fuzzy rules generation using fuzzy genetic algorithm”, IEEE Sixth International Conference on Fuzzy Systems and Konwlege Discovery, pp107-112, 2009.

      [26] Ishibuchi, H., Murata, T. and Turksen, I. B. “Single-objective and two-objective genetic Algorithms for selecting linguistic rules for pattern classification problems”, Fuzzy Sets and Systems, 89, 1997, pp. 135-150.

      [27] Quteishat A., C. P. Lim, K. S. Tan “A modified fuzzy min-max neural network with a genetic algorithm-based rule extractor for pattern classification”, IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 40, no. 3, pp. 641-650, 2010,.

      [28] G. Carpenter and A. Tan, “Rule extraction: From neural architecture to symbolic representation,” Connection Sci., vo.7, no.1, pp..3-27, 1995.

      [29] Xiuju, F. and Lipo, W. “Rule extraction using a novel gradient-based method and data dimensionality reduction”, In Proceedings of the 2002 International Joint Conference, pp. 1275-1280, 2002.

      [30] C Su, C Tseng, JSR Jang, T Visceglia, “ A hierarchical linguistic information-based model of English prosody: L2 data analysis and implications for computer-assisted language learning, Computer Speech & Language, 2018.

      [31] K S Yap, S. Y. Wong, S K Tiong, “Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection” IEEE 18th Conference on Emerging Technologies and Factory Automation (ETFA), 2013.

      [32] J. S. R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics , vol. 23, no. 3, pp. 665-685, 1993.

      [33] Y. Ding, X. Fu, “Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm,” Neurocomputing, no. 188, pp. 233-239, 2016.

      [34] Wei H., Tong Z., and Jian S. “Use of a genetic algorithm to optimize multistage erbium-doped fiber-amplifier systems with complex structures,” Opt. Express, vol. 12, no. 4, pp. 531–544, 2004.

      [35] Darwin, Charles. On the Origin of Species by means of Natural Selection, or the Preservation of Flavoured Races in the Struggle for Life (1st ed.) London : John Murray, 1859.

      [36] Eklund P. and Hoang A. “A comparative study of public supervised classifier performance on the UCI database”, Australian Journal of Intelligent Information Processing Systems 9 1-39, 2006.

      [37] Tenaga Nasional Berhad Malaysia, System Description and Operating Procedures Prai Power Station Stage 3, 14, 1999.

      [38] Babrauskas, V., Peacock, R. D. and Reneke, P. A. Defining Flashover For Fire Hazard Calculations: Part II. Fire Safety Journal, 38(7), p. 613–22, 2003.

      [39] R. W. Portier, Peacock, R. D. and Reneke, P. A., “FASTLite: Engineering Tools For Estimating Fire Growth And Smoke Transport,” Special publication 899, National Institute of Standards and Technology, 1996.

      [40] Jones, W. W. and Peacock, R. D. Technical reference guide for FAST version 18. National Institute of Standards and Technology, 1989.

      [41] Heskestad, G. Engineering relations for fire plumes. Society of Fire Protection Engineers, Technology report, p. 82-88, 1982.


 

View

Download

Article ID: 22762
 
DOI: 10.14419/ijet.v7i4.35.22762




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.