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- W4386932825 abstract "The support vector classification regression machine for K-class classification (K-SVCR) based on the “1-verses-1-verses-rest” structure is a unique multi-class classification method, and it generates ternary output {−1, 0, 1}. Since it generates ternary output, the training data requires corresponding labels. In this article, we have checked the performance of K-SVCR to explore the impact of (1) the labeling of the class and (2) the relative location of class clouds. Many artificially generated datasets and a real-world dataset are considered to understand the impact of the choice of labels and relative location of the class distribution. Accuracy is used for K-SVCR concerning the choice of class labels and the relative location of data clouds. We found that the change in the class labels—positive + , negative −, and neutral 0 affects the accuracy of classification significantly." @default.
- W4386932825 created "2023-09-22" @default.
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- W4386932825 date "2023-01-01" @default.
- W4386932825 modified "2023-10-16" @default.
- W4386932825 title "Performance Evaluation of K-SVCR in Multi-class Scenario" @default.
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- W4386932825 doi "https://doi.org/10.1007/978-981-99-5088-1_23" @default.
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