Abstract
Defining methods which map the features present in the input to a binomial class output is the core objective of pattern classification. The Modified Quick Fuzzy Hypersphere Neural Network (MQFHSNN) algorithm proposed is an extension of the existing Class-Specific Fuzzy Hypersphere Neural Network (CSFHSNN). Its membership function has been modified such that it outperforms the existing algorithms in the field of pattern classification. Fuzzy set hyperspheres are employed by MQFHSNN for pattern clustering and classes are represented by a combination of fuzzy set hypersphere. By using class labels for forming the clusters, the presented model gives 100% accuracy on the training data. The core contributors to facilitate learning in MQFHSNN are: creation of hyperspheres based on measurements provided by interclass and intraclass distance techniques as well as decision of patterns of individual classes by the fuzzy membership function. The proposed model is put into test on four standard datasets and the results obtained are more superior to the existing models. This leads us to the conclusion that MQFHSNN is being used for improvising model accuracy in pattern classification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kulkarni, U.V., Sontakke, T.R.: Fuzzy hypersphere neural network classifier. In: IEEE Conference on Fuzzy Systems, pp. 1559–1562. University of Melbourne, Australia (2001)
Simpson, P.K.: Fuzzy min-max neural networks. I. Classification. IEEE Trans. Neural Netw. 3(5), 776–786 (1992)
Simpson, P.K.: Fuzzy min-max neural networkPart 2: clustering. IEEE Trans. Fuzzy Syst. 1(1), 32–45 (1993)
Sonar, D.N., Kulkarni, U.V.: Pruned Fuzzy Hypersphere Neural Network (PFHSNN) for lung cancer classification. Int. J. Comput. Appl. 157(7), 36–39 (2017)
Kulkarni, A.B., Bonde, S.U., Kulkarni, U.V.: Inter-class and intra-class fuzzy clustering with pruning algorithm. Int. J. Comput. Sci. Eng. 6(5), 94–99 (2018)
Mohammed, M.F., Lim, C.P.: An enhanced fuzzy min-max neural network for pattern classification. IEEE Trans. Neural Netw. Learn. Syst. 26(3), 417–429 (2015)
Kulkarni, U.V., Doye, D.D., Sontakke, T.R.: General fuzzy hypersphere neural network. In: International Joint Conference on Neural Network, Honolulu, HI, USA, USA, vol. 3, pp. 2369–2374 (2002)
Doye, D.D., Kulkarni, U.V., Sontakke, T.R.: Speech recognition using modified fuzzy hypersphere neural network. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN02), Honolulu, Hawaii , vol. 1, pp. 65–68 (2002)
Kulkarni, U.V., Sontakke, T.R.: Fuzzy hypersphere neural network classifier. In: 10th IEEE International Conference on Fuzzy Systems, Melbourne, Victoria, Australia, pp. 1559–1562 (2001)
Kondekar, M.H., Kulkarni, U.V., Chowhan, S.S.: Fingerprint recognition using extended fuzzy hypersphere neural network. J. Comput. 3(4), 101–105 (2011)
Chowhan, S.S., Kulkarni, U.V., Shinde, G.N.: Iris recognition using modified fuzzy hypersphere neural network with different distance measures. Int. J. Adv. Comput. Sci. Appl. 2(6), 130–134 (2011)
Kulkarni, A.B., Bonde, S.V., Kulkarni, U.V.: Class-specific fuzzy hypersphere neural network. Proc. Comput. Sci. 143, 285–294 (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mane, D.T., Kshirsagar, J.P., Kulkarni, U.V. (2020). Modified Quick Fuzzy Hypersphere Neural Network for Pattern Classification Using Supervised Clustering. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_21
Download citation
DOI: https://doi.org/10.1007/978-981-15-2475-2_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2474-5
Online ISBN: 978-981-15-2475-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)