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Original title:
Introduction to Feature Selection Toolbox 3 – The C++ Library for Subset Search, Data Modeling and Classification
Authors: Somol, Petr ; Vácha, Pavel ; Mikeš, Stanislav ; Hora, Jan ; Pudil, Pavel ; Žid, Pavel
Document type: Research reports
Year: 2010
Language: eng
Series: Research Report, volume: 2287
Abstract: We introduce a new standalone widely applicable software library for feature selection (also known as attribute or variable selection), capable of reducing problem dimensionality to maximize the accuracy of data models, performance of automatic decision rules as well as to reduce data acquisition cost. The library can be exploited by users in research as well as in industry. Less experienced users can experiment with different provided methods and their application to real-life problems, experts can implement their own criteria or search schemes taking advantage of the toolbox framework. In this paper we first provide a concise survey of a variety of existing feature selection approaches. Then we focus on a selected group of methods of good general performance as well as on tools surpassing the limits of existing libraries. We build a feature selection framework around them and design an object-based generic software library. We describe the key design points and properties of the library.
Keywords: attribute selection; classification; feature selection; machine learning; optimization; pattern recognition; software library; subset search; variable selection
Project no.: CEZ:AV0Z10750506 (CEP), 1M0572 (CEP), 2C06019 (CEP)
Funding provider: GA MŠk, GA MŠk
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://fst.utia.cz/download/FST3_Introduction_UTIA_TR2287.pdf
Original record: http://hdl.handle.net/11104/0195589
Permalink: http://www.nusl.cz/ntk/nusl-42601
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Authors: Somol, Petr ; Vácha, Pavel ; Mikeš, Stanislav ; Hora, Jan ; Pudil, Pavel ; Žid, Pavel
Document type: Research reports
Year: 2010
Language: eng
Series: Research Report, volume: 2287
Abstract: We introduce a new standalone widely applicable software library for feature selection (also known as attribute or variable selection), capable of reducing problem dimensionality to maximize the accuracy of data models, performance of automatic decision rules as well as to reduce data acquisition cost. The library can be exploited by users in research as well as in industry. Less experienced users can experiment with different provided methods and their application to real-life problems, experts can implement their own criteria or search schemes taking advantage of the toolbox framework. In this paper we first provide a concise survey of a variety of existing feature selection approaches. Then we focus on a selected group of methods of good general performance as well as on tools surpassing the limits of existing libraries. We build a feature selection framework around them and design an object-based generic software library. We describe the key design points and properties of the library.
Keywords: attribute selection; classification; feature selection; machine learning; optimization; pattern recognition; software library; subset search; variable selection
Project no.: CEZ:AV0Z10750506 (CEP), 1M0572 (CEP), 2C06019 (CEP)
Funding provider: GA MŠk, GA MŠk
Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://fst.utia.cz/download/FST3_Introduction_UTIA_TR2287.pdf
Original record: http://hdl.handle.net/11104/0195589
Permalink: http://www.nusl.cz/ntk/nusl-42601
The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports
Record created 2011-07-04, last modified 2024-01-26