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- W3089786543 abstract "Semi-supervised learning (SSL) is a paradigm that has been continuously used in data classification tasks in datasets that do not have enough labeled instances to train a supervised model with a minimum acceptable accuracy. In this context, data stream classification in dynamic environments appears as a natural application for this approach, because changes in data distribution contribute to decrease the performance of the classification algorithms. In this paper, we have proposed a framework, refered to as Dynamic Data Stream Learning (DyDaSL), that implements an auto-adaptive classifier ensemble – which is able to evaluate and replace classifiers with decreasing performance. This platform uses the FlexCon-C method, which is a variant of the self-training SSL algorithm that adjusts a confidence threshold dynamically, in each iteration, to define which instances will be labeled. Experimental tests on synthetic and real datasets show that the proposed approach obtains better results than traditional approaches using four evaluation metrics: accuracy, F-score, precision, and recall." @default.
- W3089786543 created "2020-10-08" @default.
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- W3089786543 date "2020-07-01" @default.
- W3089786543 modified "2023-10-12" @default.
- W3089786543 title "A Semi-supervised Based Framework for Data Stream Classification in Non-Stationary Environments" @default.
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- W3089786543 doi "https://doi.org/10.1109/ijcnn48605.2020.9206792" @default.
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