An improvement on floating search algorithms for feature subset selection

Authors:
Songyot Nakariyakul;David P. Casasent
Affiliations:
Department of Electrical and Computer Engineering, Thammasat University, 99 Moo 18 Phaholyothin Rd., Ampher Khlongluang, Pathumthani 12120, Thailand;Department of Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
Venue:
Pattern Recognition
Year:
2009

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Abstract

A new improved forward floating selection (IFFS) algorithm for selecting a subset of features is presented. Our proposed algorithm improves the state-of-the-art sequential forward floating selection algorithm. The improvement is to add an additional search step called ''replacing the weak feature'' to check whether removing any feature in the currently selected feature subset and adding a new one at each sequential step can improve the current feature subset. Our method provides the optimal or quasi-optimal (close to optimal) solutions for many selected subsets and requires significantly less computational load than optimal feature selection algorithms. Our experimental results for four different databases demonstrate that our algorithm consistently selects better subsets than other suboptimal feature selection algorithms do, especially when the original number of features of the database is large.