Learning with partly labeled data |
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Authors: | Abdelhamid Bouchachia |
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Affiliation: | (1) Department of Informatics-Systems, University of Klagenfurt, Klagenfurt, Austria |
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Abstract: | Learning with partly labeled data aims at combining labeled and unlabeled data in order to boost the accuracy of a classifier.
This paper outlines the two main classes of learning methods to deal with partly labeled data: pre-labeling-based learning
and semi-supervised learning. Concretely, we introduce and discuss three methods from each class. The first three ones are
two-stage methods consisting of selecting the data to be labeled and then training the classifier using the pre-labeled and
the originally labeled data. The last three ones show how labeled and unlabeled data can be combined in a symbiotic way during
training. The empirical evaluation of these methods shows: (1) pre-labeling methods tend be better than semi-supervised learning
methods, (2) both labeled and unlabeled have positive effect on the classification accuracy of each of the proposed methods,
(3) the combination of all the methods improve the accuracy, and (4) the proposed methods compare very well with the state-of-art
methods. |
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Keywords: | Semi-supervised learning Pre-labeling Classification Clustering Ensemble classifier |
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