Abstract
The performance of fuzzy min-max classification neural network (FMM) is affected by the input sequence of training set patterns. This paper proposes a ranking-based fuzzy min-max Classification Neural Network (RFMM) to overcome this shortcoming. RFMM improves FMM through the following three aspects. First, RFMM ranks the input order of the training set patterns according to their membership degree to the center point of same class, so that the finally constructed network is fixed and does not depend on the input order of the training set. Second, a new membership function based on Manhattan distance is constructed, which overcomes the problem that the membership degree obtained by the membership function in the FMM will not decrease steadily with the increase of the distance between the input pattern and the hyperbox. At last, RFMM uses the method based on individual contour coefficient to classify the patterns in overlapping regions, which overcomes the problem that when the FMM eliminates the overlapping region by shrinking hyperboxes, the membership degree of the patterns in the contracted region to the class they belong is changed. Experimental results show that RFMM has better learning ability, and compared with other FMM methods, RFMM shows higher classification accuracy and lower network complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zheng, H., Shi, D.: Using a LSTM-RNN based deep learning framework for ICU mortality prediction. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 60–67. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_6
Wang, D., Wan, S., Guizani, N.: Context-based probability neural network classifiers realized by genetic optimization for medical decision making. Multimedia Tools and Applications 77(17), 21995–22006 (2018). https://doi.org/10.1007/s11042-018-5631-3
Xu, Z., He, T., Lian, H., Wan, J., Wang, H.: Case facts analysis method based on deep learning. In: Ni, W., Wang, X., Song, W., Li, Yukun (eds.) WISA 2019. LNCS, vol. 11817, pp. 92–97. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_11
Wang, D., Xiong, C.C., Zhang, X.K.: An opposition-based group search optimizer with diversity guidance. Math. Prob. Eng. 2015(546181), 1–12 (2015)
Zong, W., Zhou, H., Huang, G.-B., Lin, Z.: Face recognition based on kernelized extreme learning machine. In: Kamel, M., Karray, F., Gueaieb, W., Khamis, A. (eds.) AIS 2011. LNCS (LNAI), vol. 6752, pp. 263–272. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21538-4_26
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Bellman, R.E., Kalaba, R., Zadeh, L.A.: Abstraction and pattern classification. J. Math. Anal. Appl. 13, 1–7 (1966)
Wang, D., Oh, S.K., Kim, E.H.: Design of space search-optimized polynomial neural networks with the aid of ranking selection and l2-norm regularization. J. Electric. Eng. Technol. 13(4), 1724–1731 (2018)
Simpson, P.K.: Fuzzy min-max neural networks – Part1: classification. IEEE Trans. Neural Netw. 3(5), 776–786 (1992)
Sayaydeh, O.N.A., Mohammed, M.F., Lim, C.P.: Survey of Fuzzy Min-Max Neural Network for Pattern Classification Variants and Applications. IEEE Trans. Fuzzy Syst. 27(4), 635–645 (2019)
Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Netw. 11(3), 769–783 (2000)
Bargiela, A., Pedrycz, W., Tanaka, M.: An inclusion/exclusion fuzzy hyperbox classifer. Int. J. Knowl. based Intell. Eng. Syst. 8(2), 91–98 (2004)
Nandedkar, A.V., Biswas, P.K.: A fuzzy min-max neural network classifier with compensatory neuron architecture. IEEE Trans. Neural Netw. 8(1), 42–54 (2007)
Zhang, H., Liu, J., Ma, D., Wang, Z.: Data-core-based fuzzy min-max neural network for pattern classification. IEEE Trans. Neural Netw. 22(12), 2339–2352 (2011)
Davtalab, R., Dezfoulian, M.H., Mansoorizadeh, M.: Multi-level fuzzy min-max neural network classifier. IEEE Trans. Neural Netw. Learn. Syst. 25(3), 470–482 (2014)
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)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant 61673295, by the National Key R&D Program of China under Grant 2018YFC0831405, by the Natural Science Foundation of Tianjin for Distinguished Young Scholars under Grant 19JCJQJC61500, by the Natural Science Foundation of Tianjin (Key Program) under Grant 18JCZDJC30700, and by the Natural Science Foundation of Tianjin (General Program) under Grant 18JCYBJC85200, by the Major Project for New Generation of AI Grant 2018AAA0100400.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xue, L., Huang, W., Wang, J. (2020). Ranking-Based Fuzzy Min-Max Classification Neural Network. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-60029-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60028-0
Online ISBN: 978-3-030-60029-7
eBook Packages: Computer ScienceComputer Science (R0)
