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From active towards InterActive learning: using consideration information to improve labeling correctness


Bernstein, Abraham; Li, Jiwen (2009). From active towards InterActive learning: using consideration information to improve labeling correctness. In: Human Computation Workshop, Paris, France, June 2009, 40-43.

Abstract

Active learning methods have been proposed to reduce the labeling effort of human experts: based on the initially available labeled instances and information about the unlabeled data those algorithms choose only the most informative instances for labeling. They have been shown to significantly reduce the size of the required labeled dataset to generate a precise model [17]. However, active learning framework assumes "perfect" labelers, which is not true in practice (e.g., [22, 23]). In particular, an empirical study for hand-written digit recognition [5] has shown that active learning works poorly when a human labeler is used. Thus, as active learning enters the realm of practical applications, it will need to confront the practicalities and inaccuracies of human expert decision-making. Specifically, active learning approaches will have to deal with the problem that human experts are likely to make mistakes when labeling the selected instances.

Abstract

Active learning methods have been proposed to reduce the labeling effort of human experts: based on the initially available labeled instances and information about the unlabeled data those algorithms choose only the most informative instances for labeling. They have been shown to significantly reduce the size of the required labeled dataset to generate a precise model [17]. However, active learning framework assumes "perfect" labelers, which is not true in practice (e.g., [22, 23]). In particular, an empirical study for hand-written digit recognition [5] has shown that active learning works poorly when a human labeler is used. Thus, as active learning enters the realm of practical applications, it will need to confront the practicalities and inaccuracies of human expert decision-making. Specifically, active learning approaches will have to deal with the problem that human experts are likely to make mistakes when labeling the selected instances.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:June 2009
Deposited On:04 Feb 2010 11:50
Last Modified:19 May 2022 09:22
OA Status:Green
Publisher DOI:https://doi.org/10.1145/1600150.1600165

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