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- W4362734981 abstract "Abstract The design of classifiers involves learning the probability distribution of data classes from labeled samples. Traditionally, this problem assumes a single estimation of the distribution, but recent advancements in data generation have led to non-stationary data streams where the distribution is subject to concept drift and evolution. In such cases, classifiers that can incrementally update their estimates of the distribution are advantageous. Among them, clustering-based classifiers gain prominence for using unlabeled data in the update process. These classifiers generate new clusters from partially or completely unlabeled data, categorizing each as either an existing class extension or a new one. However, to determine class information through unlabeled data, these classifiers depend on assumptions about feature-class interaction that can become invalid as the distribution changes, leading to incorrect updates. To address this problem, we propose ARM-Stream, an active-learning framework for improving the reliability of clustering-based data stream classifier updates by providing label information where the process would otherwise fail. We demonstrate the effectiveness of ARM-Stream in detecting and correcting miscategorizations through experiments on synthetic and real-world datasets, integrating the framework into two clustering-based data stream classifiers from the literature: MINAS and ECHO. The source code used in the experiments is provided." @default.
- W4362734981 created "2023-04-11" @default.
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- W4362734981 date "2023-04-10" @default.
- W4362734981 modified "2023-09-25" @default.
- W4362734981 title "ARM-Stream: Active Recovery of Miscategorizations in Clustering-Based Data Stream Classifiers" @default.
- W4362734981 doi "https://doi.org/10.21203/rs.3.rs-2790828/v1" @default.
- W4362734981 hasPublicationYear "2023" @default.
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