Abstract
This paper proposes a new neural network classifier system with adaptive weight update. The system is divided into two sections namely, feature subset selection section and classification section. Genetic algorithm is introduced to complete feature subset selection to save the cost of training dataset. Classification section is inspired by a further research on the weight coefficient of membership function in “Data-Core-Based Fuzzy Min-Max Neural Network”(DCFMN).The modified classifier can improve the classification accuracy when training data is much smaller than testing data where this situation often occurs in real word due to its capacity of updating its weight coefficient while testing data online. This ability is really indispensible to classify unlabeled dataset such as field data for fault detection. The proposed modified classifier is tested on data-base available online. Results demonstrate the good qualities of this new neural network classifier.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Simpson, P.K.: Fuzzy min-max neural networks-part I: Classification. IEEE Trans. Neural Networks 3, 776–786 (1992)
Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition, Piscataway, NewYork (1992)
Sushmita, M., Sankar, K.P.: Fuzzy sets in pattern recognition and machine intelligence. Fuzzy Sets Syst. 156(3), 381–386 (2005)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst. 52(1), 21–32 (1992)
Abe, S., Lan, M.S.: A method for fuzzy rules extraction directly from numerical data and its application to pattern classification. IEEE Trans. Fuzzy Syst. 3(1), 18–28 (1995)
Jahromi, M.Z., Taheri, M.: A proposed method for learning ruleweights in fuzzy rule-based classification systems. Fuzzy Sets Syst. 159(4), 449–459 (2008)
Gabrys, B., Bargiela, A.: General fuzzy min-max neural network for clustering and classification. IEEE Trans. Neural Netwworks 11(3), 769–783 (2000)
Nandedkar, A.V., Biswas, P.K.: A fuzzy min-max neural network classifier with compensatory neuron architecture. IEEE Trans. Neural Networks 18(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 Networks 22(12), 2339–2352 (2011)
Harrag, A., Saigaa, D., Boukharouba, K., Drif, M., Bouchelaghem, A.: GA-based Feature Subset Selection Application to Arabic Speaker Recognition System. In: 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 382–387. IEEE Press, New York (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Yu, Z. (2012). A Modified Neural Network Classifier with Adaptive Weight Update and GA-Based Feature Subset Selection. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-31362-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31361-5
Online ISBN: 978-3-642-31362-2
eBook Packages: Computer ScienceComputer Science (R0)