Attribute Reduction in Rough Set Theory - Golafshan, Laleh
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The fast growth of technology and necessity of using computerized data lead the human beings to search and discover new methods in data mining vast area. This book introduces the new approach of meta-heuristic algorithms in rough set attribute reduction in order to avoid the curse of dimensionality and finding the reduce subset of datasets. Meanwhile, estimation the accuracy of the model by classification task is accomplished. The proposed method in this book proves that Reheat-SA is a powerful method for large scale combinational optimization.

Produktbeschreibung
The fast growth of technology and necessity of using computerized data lead the human beings to search and discover new methods in data mining vast area. This book introduces the new approach of meta-heuristic algorithms in rough set attribute reduction in order to avoid the curse of dimensionality and finding the reduce subset of datasets. Meanwhile, estimation the accuracy of the model by classification task is accomplished. The proposed method in this book proves that Reheat-SA is a powerful method for large scale combinational optimization.
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Autorenporträt
Laleh Golafshan received her B.Sc in Computer Engineering from IAU, Iran and M.Sc in Computer Science from UKM, Malaysia in 2005 and 2011 respectively. Her research interests are data mining, optimization and classification. Currently, she is an instructor at the Departments of Computer Engineering and IT in IAU-Fars Science and Research Branch.