Feature Selection with Adjustable Criteria | Zendy

JingTao Yao | Zendy; Ming Zhang | Zendy

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Feature Selection with Adjustable Criteria

Author(s) -

JingTao Yao,

Ming Zhang

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28653-5

DOI - 10.1007/11548669_22

Subject(s) - computer science , parameterized complexity , heuristic , rough set , set (abstract data type) , feature selection , feature (linguistics) , artificial intelligence , selection (genetic algorithm) , data mining , pattern recognition (psychology) , machine learning , algorithm , linguistics , philosophy , programming language

We present a study on a rough set based approach for feature selection. Instead of using significance or support, Parameterized Average Support Heuristic (PASH) considers the overall quality of the potential set of rules. It will produce a set of rules with balanced support distribution over all decision classes. Adjustable parameters of PASH can help users with different levels of approximation needs to extract predictive rules that may be ignored by other methods. This paper finetunes the PASH heuristic and provides experimental results to PASH.

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