Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Rough Sets in Hybrid Soft Computing Systems
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Selection of effective network parameters in attacks for intrusion detection
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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Feature subset selection is of prime importance in pattern classification, machine learning and data mining applications. Though statistical techniques are well developed and mathematically sound, they are inappropriate for dealing real world cognitive problems containing imprecise and ambiguous information. Soft computing tools like artificial neural network, genetic algorithm fuzzy logic, rough set theory and their integration in developing hybrid algorithms for handling real life problems are recently found to be the most effective. In this worka neurorough hybrid algorithm has been proposed in which rough set concepts are used for finding an initial subset of efficient features followed by a neural stage to find out the ultimate best feature subset. The reduction of original feature set results in a smaller structure and quicker learning of the neural stage and as a whole the hybrid algorithm seems to provide better performance than any algorithm from individual paradigm as is evident from the simulation results.