DATA BALANCING METHODS BY FUZZY ROUGH SETS

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Authors

Keywords:

Rough Set theory; Fuzzy-rough sets; Granular computing; Imbalanced data; Instance selection.

Abstract

The robustness of rough sets theory in data cleansing have been proved in many studies. Recently, fuzzy rough set also make a deal with imbalanced data by two approaches. The first is a combination of fuzzy rough instance selection and balancing methods. The second tries to use different criteria to clean majorities and minorities classes of imbalanced data. This work is an extension of the second method which was presented in [16]. The paper depicts complete study about the second method with some proposed algorithms. It focuses mainly on binary classification with kNN and SVM for imbalanced data. Experiments and comparisons among related methods will confirm pros and coin of each method with respect to performance accuracy and time consumption.

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Published

15-12-2020

How to Cite

Tran Thanh Huyen. “DATA BALANCING METHODS BY FUZZY ROUGH SETS”. Journal of Military Science and Technology, no. csce4, Dec. 2020, pp. 40-59, https://online.jmst.info/index.php/jmst/article/view/328.