Ensemble feature selection for high dimensional data: a new method and a comparative study

The curse of dimensionality is based on the fact that high dimensional data is often difficult to work with. A large number of features can increase the noise of the data and thus the error of a learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the...

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Bibliographic Details
Published in:Advances in data analysis and classification 2018-12, Vol.12 (4), p.937-952
Main Authors: Ben Brahim, Afef, Limam, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Chemistry and Earth Sciences
Classification
Comparative studies
Computer Science
Data Mining and Knowledge Discovery
Dimensional stability
Economics
Finance
Health Sciences
Humanities
Insurance
Law
Machine learning
Management
Mathematics and Statistics
Medicine
Physics
Product acceptance
Production planning
Regular Article
Reliability analysis
Selectors
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
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Summary:The curse of dimensionality is based on the fact that high dimensional data is often difficult to work with. A large number of features can increase the noise of the data and thus the error of a learning algorithm. Feature selection is a solution for such problems where there is a need to reduce the data dimensionality. Different feature selection algorithms may yield feature subsets that can be considered local optima in the space of feature subsets. Ensemble feature selection combines independent feature subsets and might give a better approximation to the optimal subset of features. We propose an ensemble feature selection approach based on feature selectors’ reliability assessment. It aims at providing a unique and stable feature selection without ignoring the predictive accuracy aspect. A classification algorithm is used as an evaluator to assign a confidence to features selected by ensemble members based on their associated classification performance. We compare our proposed approach to several existing techniques and to individual feature selection algorithms. Results show that our approach often improves classification performance and feature selection stability for high dimensional data sets.
ISSN:1862-5347
1862-5355
DOI:10.1007/s11634-017-0285-y