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- W4381322402 abstract "Complex evidence theory (CET), which extends Dempster Shafer theory to a higher dimensional space, is well studied these days. In CET, how to generate CBBA is crucial for uncertainty reasoning, and it is still an open issue. To be specific, CBBA can be generated through data in real number field or in the complex number field. For the data in real number field, it is necessary to mapping it to complex number field. Meanwhile, it is better to reinforce features of data in the process of mapping. In this paper, a discrete Fourier transform-based feature reinforcement method is proposed to map the data from real number field to complex number field. What's more, we propose a novel reinforcement measurement, similarity indices, to quantify the level of the feature reinforcement. The experiments illustrate that the discrete Fourier transform-based feature reinforcement method achieves good performance, which can be beneficial to pattern recognition to some extent." @default.
- W4381322402 created "2023-06-21" @default.
- W4381322402 creator A5006241046 @default.
- W4381322402 creator A5026706701 @default.
- W4381322402 date "2022-12-01" @default.
- W4381322402 modified "2023-09-28" @default.
- W4381322402 title "A Feature Reinforcement Method Based on Discrete Fourier Transform" @default.
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- W4381322402 doi "https://doi.org/10.1109/isctech58360.2022.00029" @default.
- W4381322402 hasPublicationYear "2022" @default.
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