From Active Learning to Dedicated Collaborative Interactive Learning
ISBN
978-3-8007-4157-1
Type
conference paper
Date Issued
2016-04-04
Author(s)
Calma, Adrian
Lukowicz, Paul
Oeste-Reiß, Sarah
Reitmaier, Tobias
Schmidt, Albrecht
Sick, Bernhard
Stumme, Gerd
Zweig, Katharina Anna
Research Team
IWI6
Abstract
Active learning (AL) is a machine learning paradigm where an active learner has to train a model (e.g., a classifier) which is in principle trained in a supervised way. AL has to be done by means of a data set where a low fraction of samples (also termed data points or observations) are labeled. To obtain labels for the unlabeled samples, the active learner has to ask an oracle (e.g., a human expert) for labels. In most cases, the goal is to maximize some metric assessing the task performance (e.g., the classification accuracy) and to minimize the number of queries at the same time. In this article, we first briefly discuss the state-of-the-art in the field of AL. Then, we propose the concept of dedicated collaborative interactive learning (D-CIL) and describe some research challenges. With D-CIL, we will overcome many of the harsh limitations of current AL. In particular, we envision scenarios where the expert may be wrong for various reasons. There also might be several or even many experts with different expertise who collaborate, the experts may label not only samples but also supply knowledge at a higher level such as rules, and we consider that the labeling costs depend on many conditions. Moreover, human experts may even profit by improving their own knowledge when they get feedback from the active learner.
Language
English
Keywords
Active Learning
Dedicated Collaborative Interactive Learning
HSG Classification
contribution to scientific community
Book title
Proceedings of 29th International Conference on Architecture of Computing Systems ARCS 2016
Publisher
IEEE Computer Society Press
Start page
1
End page
8
Event Title
29th International Conference on Architecture of Computing Systems (ARCS)
Event Location
Nürnberg
Event Date
04.-07.04.2016
Official URL
Division(s)
Eprints ID
247797
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