An incremental attribute reduction algorithm based on improved binary distinguishable matrix

The essence of attribute reduction is to delete unnecessary or unimportant attributes in the knowledge base while keeping the classification ability of the information system. Most of the existing reduction algorithms are designed for static decision tables. However, when the data set changes dynami...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-05, Vol.1544 (1), p.12018
Main Authors: Feng, Weibing, Ma, Yunyun
Format: Article
Language:English
Subjects:
Algorithms
Datasets
Knowledge bases (artificial intelligence)
Matrix reduction
Rough set models
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Summary:The essence of attribute reduction is to delete unnecessary or unimportant attributes in the knowledge base while keeping the classification ability of the information system. Most of the existing reduction algorithms are designed for static decision tables. However, when the data set changes dynamically, the time consumption of static reduction algorithms is huge and the efficiency is not high. At present, there are few studies on the reduction algorithms of dynamic decision tables. In this paper, an existing binary distinguishable matrix reduction algorithm is optimized by using the method of binary distinguishable matrix when the objects are dynamically increased. In the case of static data set, the algorithm is improved to deal with dynamic data set by judging the relationship between new data and original data set. Examples show that the algorithm is simple, efficient, accurate, and has certain practicability and completeness.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1544/1/012018