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Published in:
2017 | OriginalPaper | Chapter
Exploration of Heuristic-Based Feature Selection on Classification Problems
Authors : Qi Qi, Ni Li, Weimin Li
Published in: Parallel Architecture, Algorithm and Programming
Publisher: Springer Singapore
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Abstract
We present two heuristics for feature selection based on entropy and mutual information criteria, respectively. The mutual-information-based selection algorithm exploiting its submodularity retrieves near-optimal solutions guaranteed by a theoretical lower bound. We demonstrate that these heuristic-based methods can reduce the dimensionality of classification problems by filtering out half of its features in the meantime still improving classification accuracy. Experimental results also show that the mutual-information-based heuristic will most likely collaborate well with classifiers when selecting about a half size of features, while the entropy-based heuristic will help most in the early stage of selection when choosing a relatively small percentage of features. We also demonstrate a remarkable case of feature selection being used in classification on a medical dataset, where it can potentially save half of the cost on the diabetes diagnosis.