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- W3184441495 abstract "AbstractOne-class classification is machine learning problem where training data has only one class. Recently, various subtask-based one-class classification methods are proposed for image data. However, these methods are specialized for image. Applying them to feature data is difficult. In this paper, feature-slide prediction subtask is proposed for feature data. These additional feature vectors are annotated as how features are slid. Then, multi-class classifier is trained using self-labeled dataset. Since this classification model is built using data from only one-class, accuracy of feature-slide prediction for seen data is high relative to unseen data. Accordingly, OCC could be made using feature slide prediction subtask. Proposed methods are experimented using imbalanced-learn dataset.KeywordsMachine learningOne-class classificationSubtask" @default.
- W3184441495 created "2021-08-02" @default.
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- W3184441495 date "2021-01-01" @default.
- W3184441495 modified "2023-09-27" @default.
- W3184441495 title "One-Class Classification Approach Using Feature-Slide Prediction Subtask for Feature Data" @default.
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- W3184441495 doi "https://doi.org/10.1007/978-3-030-79463-7_8" @default.
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