Journal
KNOWLEDGE-BASED SYSTEMS
Volume 153, Issue -, Pages 53-64Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2018.04.023
Keywords
Rough set; Local neighborhood rough set; Concept approximation; Attribute reduction; Limited labeled data
Categories
Funding
- National Natural Science Foundation of China [61672332, 61322211, 61432011, U1435212, 11671006, 61603173]
- Program for New Century Excellent Talents in University [NCET-12-1031]
- Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi
- Program for the Young San Jin Scholars of Shanxi
- National Key Basic Research and Development Program of China (973) [2013CB329404, 2013CB329502]
- Key Science and Technology Program of Shanxi [MQ2014-09]
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With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes. (C) 2018 Elsevier B.V. All rights reserved.
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