Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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IEEE Transactions on Knowledge and Data Engineering
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Journal of Biomedical Informatics
Information Sciences: an International Journal
A hybrid approach to feature subset selection for brain-computer interface design
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A tutorial on human activity recognition using body-worn inertial sensors
ACM Computing Surveys (CSUR)
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Among recent topics studied in context of feature selection the hybrid algorithms seem to receive particular attention. In this paper we propose a new hybrid algorithm, the flexible hybrid floating sequential search algorithm, that combines both the filter and wrapper search principles. The main benefit of the proposed algorithm is its ability to deal flexibly with the quality-of-result versus computational time trade-off and to enable wrapper based feature selection in problems of higher dimensionality than before. We show that it is possible to trade significant reduction of search time for negligible decrease of the classification accuracy. Experimental results are reported on two data sets, WAVEFORM data from the UCI repository and SPEECH data from British Telecom.