Abstract
Unsupervised neural network based pattern classification is a widely popular choice for many real time applications. Such applications always face challenges of processing data with lot of consistency, inconsistency, ambiguity or incompleteness. Hence to deal with such challenges a strong approximation tool is always needed. Rough set is one such tool and various approaches based on Rough set, if are applied to pure neural (unsupervised) pattern classifier can yield desired results like faster convergence, feature space reduction and improved classification accuracy. The application of such approaches at respective level of implementation of neural network based pattern classifier for two case studies are discussed here. Whereas more emphasis is given on the preprocessing level based approach used for feature space reduction.
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
Zdzislaw Pawlak: “ROUGH SETS – Theoretical Aspects of Reasoning About Data”, 1992, Kluwer, Dordrecht, pages 1–43.
Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddiins: “Digital Image Processing Using MATLAB”, First Impression, 2006, Pearson Education, NJ, USA, pages 348–497.
Jianming Hu, Donggang Yu, Hong Yan: “Algorithm for stroke width compensation of handwritten characters”, Electronics Letters Online No. 19961501.
Hongsheng Su, Qunzhan Li: “Fuzzy Neural Classifier for Fault Diagnosis of Transformer Based on Rough Sets Theory”: IEEE, CS, 2223 to 2227.
Giorgos Vamvakas: “Optical Character Recognition for Handwritten Characters”: National Center for Scientific Research, Demokritos Athens – Greece, Institute of Informatics and Telecommunications and Computational Intelligence Laboratory (CIL).
Jiang-Hong Man: “An Improved Fuzzy Discretization Way for Decision Tables with Continues Attributes”, Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, 19–22 August 2007.
S.N. Sivanandam, S. Sumathi, S.N. Deepa: “Introduction to Neural Networks Using Matlab 6.0” first edition, 2006, Tata MCGraw Hill, OH, USA, pages 531–536.
J.S.R. Jang, C. T Sun, E. Mizutani: “Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, First edition reprint 2005, Pearson education, NJ, USA, pages 327–331.
Xian-Ming Huang, Ji-Kai Yi, Yan-Hong Zhang: “A method of constructing fuzzy neural network based on rough set theory”, International Conference on Machine Learning and Cybernetics, 2003, Publication Date: 2–5 Nov. 2003, Volume: 3, pages 1723–1728.
Ashwin G. Kothari: “Data Mining Tool for Semiconductor Manufacturing Using Rough Neuro Hybrid approach”, Proceedings of International Conference on Computer Aided engineering-CAE-2007, IIT Chennai, 13–15 December 2007.
C. Sandeep, R. Mayoraga: “Rough Set Based Neural Network Architecture”, International Joint Conference on Neural Networks, Vancouver, BC, Canada, 2006.
Pawan Lingras: “Rough Neural Network,” Proceedings of the 6th International Conference on Information Processing and Management of Uncertainty, Granada, pages 1445–1450, 1996.
Acknowledgment
The support of Dr. A.P. Gokhale and the team of students consisting of Mr. Bharthan Balaji, Mr. Pradeep Dhananjay, Ms. Y.T. Vasavdatta and Ms. Deepti pant is highly acknowledged for carrying out the experimentation and acquiring of data in the lab.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Kothari, A., Keskar, A. (2010). Rough Set Approaches to Unsupervised Neural Network Based Pattern Classifier. In: Amouzegar, M. (eds) Advances in Machine Learning and Data Analysis. Lecture Notes in Electrical Engineering, vol 48. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3177-8_10
Download citation
DOI: https://doi.org/10.1007/978-90-481-3177-8_10
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-3176-1
Online ISBN: 978-90-481-3177-8
eBook Packages: EngineeringEngineering (R0)