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Active learning in the presence of unlabelable examplesWe propose a new active learning framework where the expert labeler is allowed to decline to label any example. This may be necessary because the true label is unknown or because the example belongs to a class that is not part of the real training problem. We show that within this framework, popular active learning algorithms (such as Simple) may perform worse than random selection because they make so many queries to the unlabelable class. We present a method by which any active learning algorithm can be modified to avoid unlabelable examples by training a second classifier to distinguish between the labelable and unlabelable classes. We also demonstrate the effectiveness of the method on two benchmark data sets and a real-world problem.
Document ID
Document Type
Conference Paper
External Source(s)
Mazzoni, Dominic
Wagstaff, Kiri
Date Acquired
August 23, 2013
Publication Date
September 20, 2004
Meeting Information
European Conference on Machine Learning(Pisa)
Distribution Limits
active learning
machine learning

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