Distance Based On Neighborhood Classifier and Attribute Reduction

In the neighborhood rough set model, with the increasing of the size of information granules, the neighborhood classifier based on the majority voting rule is easy to misjudge the classes of testing samples. To remedy this deficiency, a strategy of attribute reduction based on the idea of the minimu...

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Bibliographic Details
Published in2018 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 1; pp. 257 - 262
Main Authors Gao, Yuan, Liu, Ke-Yu, Song, Jing-Jing, Chen, Xiang-Jian, Yang, Xi-Bei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Attribute reduction
Classification
Distance
Entropy
Heuristic algorithms
Machine learning algorithms
Neighborhood rough set
Prediction algorithms
Rough sets
Testing
Training
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Summary:In the neighborhood rough set model, with the increasing of the size of information granules, the neighborhood classifier based on the majority voting rule is easy to misjudge the classes of testing samples. To remedy this deficiency, a strategy of attribute reduction based on the idea of the minimum mean distance is proposed in this paper. Firstly, a neighborhood relation is presented by a distance function. Secondly, neighborhood mean distance classifier judges the class of testing sample with the minimum mean distance instead of the majority voting rule in the decision system. Finally, the experimental results on 8 UCI data sets tell us that the reduct obtained by our strategy can not only decrease the conditional entropy, but also provide better classification performances in larger scale information granules.
ISSN:2160-1348
DOI:10.1109/ICMLC.2018.8527025