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• W2745243248 abstract "Traditional feature selection methods cannot achieve good performance on imbalanced data sets. A new feature selection method for imbalanced data sets is presented according to the characteristics of radio signals of ground-air communication. Firstly, many sample subsets are randomly selected from the sample set of the major class using Bagging algorithm, the sample size of each selected sample subset is equal to that of the minor class, and multiple new training sets are composed of the selected sample subsets and the minor class respectively. Secondly, many feature subsets are got by using the feature selection approach based on ReliefF and clustering. Finally, the final feature subset is generated by the way of integrated voting. Experiments show that the proposed algorithm is effective on radio signal recognition of ground-air communication. It can not only reduce the feature dimension but also improve the signal recognition rate of the minor class effectively." @default.
• W2745243248 created "2017-08-17" @default.
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• W2745243248 date "2016-11-01" @default.
• W2745243248 modified "2023-09-23" @default.
• W2745243248 title "Feature selection method for imbalanced data with application in radio signal recognition of ground-air communication" @default.
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• W2745243248 doi "https://doi.org/10.1109/ifuzzy.2016.8004929" @default.
• W2745243248 hasPublicationYear "2016" @default.
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