Learning from ambiguously labeled examples

Authors:
Eyke Hüllermeier;Jürgen Beringer
Affiliations:
Fakultät für Informatik, Otto-von-Guericke-Universität Magdeburg, Germany;Fakultät für Informatik, Otto-von-Guericke-Universität Magdeburg, Germany
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

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Abstract

Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice.