On the Properties and Applications of Inconsistent Neighborhood in Neighborhood Rough Set Models

Shujiao LIAO; Qingxin ZHU; Rui LIANG

doi:10.1587/transinf.2017EDP7238

On the Properties and Applications of Inconsistent Neighborhood in Neighborhood Rough Set Models

Shujiao LIAO, Qingxin ZHU, Rui LIANG

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Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting properties are obtained for inconsistent neighborhoods. Specially, some properties generate new solutions to compute the quantities in neighborhood rough set. Then, a fast forward attribute reduction algorithm is proposed by applying the obtained properties. Experiments undertaken on twelve UCI datasets show that the proposed algorithm can get the same attribute reduction results as the existing algorithms in neighborhood rough set domain, and it runs much faster than the existing ones. This validates that employing inconsistent neighborhoods is advantageous in the applications of neighborhood rough set. The study would provide a new insight into neighborhood rough set theory.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.3 pp.709-718

Publication Date: 2018/03/01

Publicized: 2017/12/20

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7238

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Shujiao LIAO
  University of Electronic Science and Technology of China,Minnan Normal University
Qingxin ZHU
  University of Electronic Science and Technology of China
Rui LIANG
  University of Electronic Science and Technology of China

Keyword

inconsistent neighborhood, neighborhood rough set, properties, attribute reduction, fast forward algorithm, run-time

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Shujiao LIAO, Qingxin ZHU, Rui LIANG, "On the Properties and Applications of Inconsistent Neighborhood in Neighborhood Rough Set Models" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 3, pp. 709-718, March 2018, doi: 10.1587/transinf.2017EDP7238.
Abstract: Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting properties are obtained for inconsistent neighborhoods. Specially, some properties generate new solutions to compute the quantities in neighborhood rough set. Then, a fast forward attribute reduction algorithm is proposed by applying the obtained properties. Experiments undertaken on twelve UCI datasets show that the proposed algorithm can get the same attribute reduction results as the existing algorithms in neighborhood rough set domain, and it runs much faster than the existing ones. This validates that employing inconsistent neighborhoods is advantageous in the applications of neighborhood rough set. The study would provide a new insight into neighborhood rough set theory.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7238/_p

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@ARTICLE{e101-d_3_709,
author={Shujiao LIAO, Qingxin ZHU, Rui LIANG, },
journal={IEICE TRANSACTIONS on Information},
title={On the Properties and Applications of Inconsistent Neighborhood in Neighborhood Rough Set Models},
year={2018},
volume={E101-D},
number={3},
pages={709-718},
abstract={Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting properties are obtained for inconsistent neighborhoods. Specially, some properties generate new solutions to compute the quantities in neighborhood rough set. Then, a fast forward attribute reduction algorithm is proposed by applying the obtained properties. Experiments undertaken on twelve UCI datasets show that the proposed algorithm can get the same attribute reduction results as the existing algorithms in neighborhood rough set domain, and it runs much faster than the existing ones. This validates that employing inconsistent neighborhoods is advantageous in the applications of neighborhood rough set. The study would provide a new insight into neighborhood rough set theory.},
keywords={},
doi={10.1587/transinf.2017EDP7238},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - On the Properties and Applications of Inconsistent Neighborhood in Neighborhood Rough Set Models
T2 - IEICE TRANSACTIONS on Information
SP - 709
EP - 718
AU - Shujiao LIAO
AU - Qingxin ZHU
AU - Rui LIANG
PY - 2018
DO - 10.1587/transinf.2017EDP7238
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2018
AB - Rough set theory is an important branch of data mining and granular computing, among which neighborhood rough set is presented to deal with numerical data and hybrid data. In this paper, we propose a new concept called inconsistent neighborhood, which extracts inconsistent objects from a traditional neighborhood. Firstly, a series of interesting properties are obtained for inconsistent neighborhoods. Specially, some properties generate new solutions to compute the quantities in neighborhood rough set. Then, a fast forward attribute reduction algorithm is proposed by applying the obtained properties. Experiments undertaken on twelve UCI datasets show that the proposed algorithm can get the same attribute reduction results as the existing algorithms in neighborhood rough set domain, and it runs much faster than the existing ones. This validates that employing inconsistent neighborhoods is advantageous in the applications of neighborhood rough set. The study would provide a new insight into neighborhood rough set theory.
ER -