Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 76, Issue -, Pages 80-95Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2016.05.001
Keywords
Decision system; Knowledge granularity; Attribute reduction; Incremental learning; Rough set theory
Categories
Funding
- National Science Foundation of China [61573292, 61572406]
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Attribute reduction is a key step to discover interesting patterns in the decision system with numbers of attributes available. In recent years, with the fast development of data processing tools, the information system may increase quickly in attributes over time. How to update attribute reducts efficiently under the attribute generalization becomes an important task in knowledge discovery related tasks since the result of attribute reduction may alter with the increase of attributes. This paper aims for investigation of incremental attribute reduction algorithm based on knowledge granularity in the decision system under the variation of attributes. Incremental mechanisms to calculate the new knowledge granularity are first introduced. Then, the corresponding incremental algorithms are presented for attribute reduction based on the calculated knowledge granularity when multiple attributes are added to the decision system. Finally, experiments performed on UCI data sets and the complexity analysis show that the proposed incremental methods are effective and efficient to update attribute reducts with the increase of attributes. (C) 2016 Elsevier Inc. All rights reserved.
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