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Entropy 2019, 21(2), 155; https://doi.org/10.3390/e21020155

An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets

1
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2
Engineering Technology Research Center for Computing Intelligence and Data Mining, Henan 453007, China
*
Author to whom correspondence should be addressed.
Received: 8 December 2018 / Revised: 22 January 2019 / Accepted: 1 February 2019 / Published: 7 February 2019
(This article belongs to the Section Signal and Data Analysis)
PDF [669 KB, uploaded 7 February 2019]   |   Review Reports

Abstract

Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.
Keywords: rough sets; neighborhood rough sets; attribute reduction; neighborhood entropy; classification rough sets; neighborhood rough sets; attribute reduction; neighborhood entropy; classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, L.; Zhang, X.; Xu, J.; Zhang , S. An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets. Entropy 2019, 21, 155.

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