A Novel Neuro-fuzzy Classification Technique

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In our study, we proposed a novel Neuro-fuzzy classification technique. The inputs to the Neuro-fuzzy classification system were fuzzified by applying the Gaussian curve membership function. The proposed method considered a fuzzification matrix in which the input features were associated with a degree of membership to different classes. Based on the value of degree of membership a feature would be attributed to a specific category or class. We applied our method to five benchmark datasets from the UCI machine language repository for classification. Our objective was to analyze the proposed method and, therefore compare its performance with Multilayer Perceptron Backpropagation Network (MLPBPN) algorithm in terms of different performance measures like Accuracy, Root-mean-square error, Kappa statistic, True Positive Rate, False Positive Rate, Precision, Recall, and F-Measure. In every aspect the proposed method performed better than MLPBPN.

ATA mining has attracted many researchers and analysts in the information industry and in research organizations as a whole in the last decades, due to the availability of large amounts of data and the impending need for changing such data into meaningful information and knowledge. The useful information and knowledge gained can be used for applications ranging from market survey, customer retention, and production control to evolutionary analysis and science exploration [1], [2].
Classification as an important data mining technique involves extracting interesting patterns representing knowledge from large real-world databases. Such analysis can provide a deep insight into the better understanding of different large-scale databases. The study related to effective knowledge development is ...

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...” McGraw-Hill, 1996.
[8] P. J. Werbos, “The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting,” New York, NY: John Wiley & Sons, 1994.
[9] L. A. Zadeh "Fuzzy sets". Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[10] B. Liu, "Uncertain theory: an introduction to its axiomatic foundations," Berlin: Springer-Verlag, 2004.
[11] D. Dubois and H. Prade, “Fuzzy Sets and Systems,” Academic Press, New York, 1988.
[12] K. Elis J.-S.R. Jang, C.-T. Sun, and E. Mizutani, “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence,” Prentice Hall, USA, 1997.
[13] C.-T. Lin and C. S. George Lee, “Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems ,” Prentice Hall, 1996.
[14] D. Nauck, F. Klawonn, and R. Kruse, “Foundations of Neuro fuzzy Systems,” Wiley, Chichester, 1997.
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