Abstract
Deep Learning is a popular and promising technique for classification problems. This paper proposes the use of fuzzy deep learning to improve the classification capability when dealing with overlapped data. Most of the research focuses on classification and uses traditional truth and false criteria. However, in reality, a data item may belong to different classes at different degrees. Therefore, the degree of belonging of each data item to a class needs to be considered for classification purposes in some cases. When a data item belongs to different classes with different degrees, then there exists an overlap between the classes. For this reason, this paper proposes a Fuzzy Deep Neural Network based on Fuzzy C-means clustering, fuzzy membership grades and Deep Neural Networks to address the over-lapping issue focused on binary classes and multi-classes. The proposed method converts the original attribute values to relevant cluster centres using the proposed Fuzzy Deep Neural Network. It then trains them with the original output class values. Thereafter, the test data is checked with the Fuzzy Deep Neural Network model for its performance. Using three popular datasets in overlapped and fuzzy data literature, the method presented in this paper outperforms the other methods compared in this study, which are Deep Neural Networks and Fuzzy classification.
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References
Xiong, H., et al.: Classification algorithm based on NB for class overlapping problem. Appl. Math. Inf. Sci. 7(2L), 409–415 (2013)
Tang, W., et al.: Classification for overlapping classes using optimized overlapping region detection and soft decision. In: 13th Conference on Information Fusion (FUSION). IEEE (2010)
Lee, H.K., Kim, S.B.: An overlap-sensitive margin classifier for imbalanced and overlapping data. Expert Syst. Appl. 98, 72–83 (2018)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Setyohadi, D.B., Bakar, A.A., Othman, Z.A.: Optimization overlap clustering based on the hybrid rough discernibility concept and rough K-Means. Intell. Data Anal. 19(4), 795–823 (2015)
Banerjee, A., et al.: Model-based overlapping clustering. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM (2005)
Faust, O., et al.: Deep learning for healthcare applications based on physiological signals: a review. Comput. Methods Programs Biomed. 161, 1–13 (2018)
Young, T., et al.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55–75 (2018)
LeCun, Y., Bengio, Y., Hinton, G.: Deep. Learn. Nat. 521(7553), 436 (2015)
Patwary, M.J., Wang, X.-Z.: Sensitivity analysis on initial classifier accuracy in fuzziness based semi-supervised learning. Inf. Sci. 490, 93–112 (2019)
Wang, C.-S., et al.: Detecting potential adverse drug reactions using a deep neural network model. J. Med. Internet Res. 21(2), e11016 (2019)
Kim, H.-C., Bandettini, P.A., Lee, J.-H.: Deep neural network predicts emotional responses of the human brain from functional magnetic resonance imaging. NeuroImage 186, 607–627 (2019)
Saleem, N., et al.: deep neural network for supervised single-channel speech enhancement. Arch. Acoust. 44(1), 3–12 (2019)
Katzman, J.L., et al.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018)
Sumit, S.H., Akhter, S.: C-means clustering and deep-neuro-fuzzy classification for road weight measurement in traffic management system. Soft Comput. 23, 4329–4340 (2019). https://doi.org/10.1007/s00500-018-3086-0
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
De Silva, C.W.: Intelligent Control: Fuzzy Logic Applications. CRC Press, Boca Raton (2018)
Korenevskiy, N.: Application of fuzzy logic for decision-making in medical expert systems. Biomed. Eng. 49(1), 46–49 (2015)
Dotcenko, S., Vladyko, A., Letenko, I.: A fuzzy logic-based information security management for software-defined networks. In: 16th International Conference on Advanced Communication Technology (ICACT), 2014. IEEE (2014)
Park, S., et al.: Intra-and inter-fractional variation prediction of lung tumors using fuzzy deep learning. IEEE J. Transl. Eng. Health Med. 4, 1–12 (2016)
El Hatri, C., Boumhidi, J.: Fuzzy deep learning based urban traffic incident detection. Cogn. Syst. Res. 50, 206–213 (2018)
Davoodi, R., Moradi, M.H.: Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. J. Biomed. Inform. 79, 48–59 (2018)
Zheng, Y.-J., et al.: Airline passenger profiling based on fuzzy deep machine learning. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 2911–2923 (2017)
Deng, Y., et al.: A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans. Fuzzy Syst. 25(4), 1006–1012 (2017)
Chen, C.P., et al.: Fuzzy restricted Boltzmann machine for the enhancement of deep learning. IEEE Trans. Fuzzy Syst. 23(6), 2163–2173 (2015)
Nugaliyadde, A., Pruengkarn, R., Wong, K.W.: The fuzzy misclassification analysis with deep neural network for handling class noise problem. In: Cheng, L., Leung, A.C.S., Ozawa, S. (eds.) ICONIP 2018. LNCS, vol. 11304, pp. 326–335. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04212-7_28
Mak, L.O., et al.: A merging Fuzzy ART clustering algorithm for overlapping data. In: 2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI). IEEE (2011)
Xiong, H., Wu, J., Liu, L.: Classification with class overlapping: a systematic study. In: The 2010 International Conference on E-Business Intelligence (2010)
Vorraboot, P., et al.: Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms. Neurocomputing 152, 429–443 (2015)
Das, B., Krishnan, N.C., Cook, D.J.: Handling class overlap and imbalance to detect prompt situations in smart homes. In: 2013 IEEE 13th International Conference on Data Mining Workshops. IEEE (2013)
Harris, C.J., Moore, C.G., Brown, M.: Intelligent Control: Aspects of Fuzzy Logic and Neural Nets, vol. 6. World Scientific (1993)
Dua, D., Taniskidou, E.K.: UCI Machine Learning Repository (2017). http://archive.ics.uci.edu/ml
Singh, H.R., Biswas, S.K., Purkayastha, B.: A neuro-fuzzy classification technique using dynamic clustering and GSS rule generation. J. Comput. Appl. Math. 309, 683–694 (2017)
Liu, X., et al.: A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput. Math. Methods Med. 2017 (2017). 11 pages, Article ID 8272091. https://doi.org/10.1155/2017/8272091
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Géron, A.: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media Inc, Sebastopol (2017)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from ovrfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
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Dabare, R., Wong, K.W., Shiratuddin, M.F., Koutsakis, P. (2019). Fuzzy Deep Neural Network for Classification of Overlapped Data. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_52
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