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Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
The book will
1) In depth explanation of rough set theory along with examples of the concepts.
2) Detailed discussion on idea of feature selection.
3) Details of various representative and state of the art feature selection techniques along with algorithmic explanations.
4) Critical review of state of the art rough set based feature selection methods covering strength and weaknesses of each.
5) In depth investigation of various application areas using rough set based feature selection.
6) Complete Library of Rough Set APIs along with complexity analysis and detailed manual of using APIs
7) Program files of various representative Feature Selection algorithms along with explanation of each.
The book will be a complete and self-sufficient source both for primary and secondary audience. Starting from basic concepts to state-of-the art implementation, it will be a constant source of help both for practitioners and researchers. Book will provide in-depth explanation of concepts supplemented with working examples to help in practical implementation. As far as practical implementation is concerned, the researcher/practitioner can fully concentrate on his/her own work without any concern towards implementation of basic RST functionality.
Providing complexity analysis along with full working programs will further simplify analysis and comparison of algorithms.
1) In depth explanation of rough set theory along with examples of the concepts.
2) Detailed discussion on idea of feature selection.
3) Details of various representative and state of the art feature selection techniques along with algorithmic explanations.
4) Critical review of state of the art rough set based feature selection methods covering strength and weaknesses of each.
5) In depth investigation of various application areas using rough set based feature selection.
6) Complete Library of Rough Set APIs along with complexity analysis and detailed manual of using APIs
7) Program files of various representative Feature Selection algorithms along with explanation of each.
The book will be a complete and self-sufficient source both for primary and secondary audience. Starting from basic concepts to state-of-the art implementation, it will be a constant source of help both for practitioners and researchers. Book will provide in-depth explanation of concepts supplemented with working examples to help in practical implementation. As far as practical implementation is concerned, the researcher/practitioner can fully concentrate on his/her own work without any concern towards implementation of basic RST functionality.
Providing complexity analysis along with full working programs will further simplify analysis and comparison of algorithms.
310 pages, Kindle Edition
Published June 28, 2017
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