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Data-guided multi-granularity selector for attribute reduction
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DOI：10.1007/s10489-020-01846-6.png)
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Presently, the greedy searching strategy has been widely accepted for obtaining reduct in the field of rough set. In the framework of greedy searching, the evaluation of the candidate attribute is crucial, because the evaluation can determine the final result of reduct to a large extent. However, most of the previous evaluations are designed by considering one and only one fixed granularity, which fails to make the multi-view based evaluation possible. To fill such gap, a Parameterized Multi-granularity Attribute Selector is proposed for obtaining reduct in this paper. Our attribute selector consists of two parts: one is the multi-granularity attribute selector which evaluates and selects attributes through using the information provided by multiple different granularities; the other is the data-guided parameterized granularity selector which generates multiple different parameterized granularities through taking the characteristics of data into account. The experimental results over 15 UCI data sets show the following: 1) compared with the state of the art approaches for obtaining reducts, our proposed attribute selector can contribute to reduct with higher stability; 2) our proposed attribute selector will not provide the reduct with poorer classification performance. This research suggests a new trend for the multi-granularity mechanism in the problem of attribute reduction.
Keyword:
Attribute reduction
Data-guided
Parameterized multi-granularity
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