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- W3120769754 abstract "Our ability to learn accurate classification models from data is often limited by the number of available labeled data instances. This limitation is of particular concern when data instances need to be manually labeled by human annotators and when the labeling process carries a significant cost. Recent years witnessed increased research interest in developing methods in different directions capable of learning models from a smaller number of examples. One such direction is active learning, which finds the most informative unlabeled instances to be labeled next. Another, more recent direction showing a great promise utilizes enriched label-related feedback. In this case, such feedback from the human annotator provides additional information reflecting the relations among possible labels. The cost of such feedback is often negligible compared with the cost of instance review. The enriched label-related feedback may come in different forms. In this work, we propose, develop and study classification models for binary, multi-class and multi-label classification problems that utilize the different forms of enriched label-related feedback. We show that this new feedback can help us improve the quality of classification models compared with the standard class-label feedback. For each of the studied feedback forms, we also develop new active learning strategies for selecting the most informative unlabeled instances that are compatible with the respective feedback form, effectively combining two approaches for reducing the number of required labeled instances. We demonstrate the effectiveness of our new framework on both simulated and real-world datasets." @default.
- W3120769754 created "2021-01-18" @default.
- W3120769754 creator A5020010353 @default.
- W3120769754 date "2020-09-16" @default.
- W3120769754 modified "2023-09-25" @default.
- W3120769754 title "Active Learning of Classification Models from Enriched Label-related Feedback" @default.
- W3120769754 hasPublicationYear "2020" @default.
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