A Rough Set Theory based Approach for Massive Data Mining

Li, Xianyang; Qiu, Guihua; Lu, Anshan

doi:10.14257/ijsip.2018.11.1.02

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2021

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“…The experimental results are shown in Table 3. The table records amount of after attribute reduction of each data set [12]. The second and third columns in the table respectively list the SVM classification accuracy rate before attribute reduction and the SVM classification accuracy rate after attribute reduction.…”

Section: Experimental Analysismentioning

confidence: 99%

The Improvement of Attribute Reduction Algorithm Based on Information Gain Ratio in Rough Set Theory

Wang

Guo

Han

et al. 2021

Business Intelligence and Information Technology

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Due to the various data sets and the cumbersome and diverse data types, there must be many redundant attributes in them, which greatly increases the classification time in the background of rough set theory. In this paper, we improve the attribute reduction algorithm by information gain ratio. The data sets obtained after the attribute reduction of this method are used for classification, and the data sets are directly used for classification and comparison with other common classification methods. Experimental data verify that the improved algorithm in this article is effective. It can improve the classification speed greatly and shorten the time spent.

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Section: Experimental Analysismentioning

confidence: 99%