Heuristic Ensemble of Filters for Reliable Feature Selection

Aldehim, Ghadah, De La Iglesia, Beatriz and Wang, Wenjia (2014) Heuristic Ensemble of Filters for Reliable Feature Selection. In: International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014), 2014-05-10.

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Feature selection has become ever more important in data mining in recent years due to the rapid increase in the dimensionality of data. Filters are preferable in practical applications as they are much faster than wrapper based approaches, but their reliability and consistency vary considerably on different data and yet no rule exists to indicate which one should be used for a particular given dataset. In this paper, we propose a heuristic ensemble approach that combines multiple filters with heuristic rules to improve the overall performance. It consists of two types of filters: subset filters and ranking filters, and a heuristic consensus algorithm. The experimental results demonstrate that our ensemble algorithm is more reliable and effective than individual filters as the features selected by the ensemble consistently achieve better accuracy for typical classifiers on various datasets.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: feature selection,filter,ensemble
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: Pure Connector
Date Deposited: 08 Sep 2014 12:46
Last Modified: 06 May 2022 04:02
URI: https://ueaeprints.uea.ac.uk/id/eprint/49929

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