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Sankar K. Pal, Sushmita Mitra, Pabitra Mitra, "RoughFuzzy MLP: Modular Evolution, Rule Generation, and Evaluation," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 1, pp. 1425, January/February, 2003.  
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@article{ 10.1109/TKDE.2003.1161579, author = {Sankar K. Pal and Sushmita Mitra and Pabitra Mitra}, title = {RoughFuzzy MLP: Modular Evolution, Rule Generation, and Evaluation}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {15}, number = {1}, issn = {10414347}, year = {2003}, pages = {1425}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2003.1161579}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  RoughFuzzy MLP: Modular Evolution, Rule Generation, and Evaluation IS  1 SN  10414347 SP14 EP25 EPD  1425 A1  Sankar K. Pal, A1  Sushmita Mitra, A1  Pabitra Mitra, PY  2003 KW  Soft computing KW  knowledgebased fuzzy networks KW  rough sets KW  genetic algorithms KW  pattern recognition KW  rule extraction/evaluation KW  knowledge discovery KW  data mining. VL  15 JA  IEEE Transactions on Knowledge and Data Engineering ER   
Abstract—A methodology is described for evolving a Roughfuzzy multi layer perceptron with modular concept using a genetic algorithm to obtain a structured network suitable for both classification and rule extraction. The modular concept, based on "divide and conquer" strategy, provides accelerated training and a compact network suitable for generating a minimum number of rules with high certainty values. The concept of variable mutation operator is introduced for preserving the localized structure of the constituting knowledgebased subnetworks, while they are integrated and evolved. Rough set dependency rules are generated directly from the real valued attribute table containing fuzzy membership values. Two new indices viz., "certainty" and "confusion" in a decision are defined for evaluating quantitatively the quality of rules. The effectiveness of the model and the rule extraction algorithm is extensively demonstrated through experiments alongwith comparisons.
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