Learning with Multiple Labels

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Learning with Multiple Labels (2003)

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by Rong Jin , Zoubin Ghahramani

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BibTeX

@MISC{Jin03learningwith,
    author = {Rong Jin and Zoubin Ghahramani},
    title = {Learning with Multiple Labels},
    year = {2003}
}

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Abstract

In this paper, we study a special kind of learning problem in which each training instance is given a set of (or distribution over) candidate class labels and only one of the candidate labels is the correct one. Such a problem can occur, e.g., in an information retrieval setting where a set of words is associated with an image, or if classes labels are organized hierarchically. We propose a novel discriminative approach for handling the ambiguity of class labels in the training examples. The experiments with the proposed approach over five different UCI datasets show that our approach is able to find the correct label among the set of candidate labels and actually achieve performance close to the case when each training instance is given a single correct label. In contrast, naive methods degrade rapidly as more ambiguity is introduced into the labels.

Keyphrases

multiple label candidate label training instance class label naive method special kind information retrieval single correct label candidate class label novel discriminative approach training example correct label different uci datasets

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