Batista, Antônio J.L., Campello, Ricardo J.G.B., and Sander, Jörg (2016) Active semi-supervised classification based on multiple clustering hierarchies. In: Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics. pp. 11-20. From: DSAA 2016: 3rd IEEE International Conference on Data Science and Advanced Analytics, 17-19 October 2016, Montréal, Canada.

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Abstract

Active semi-supervised learning can play an important role in classification scenarios in which labeled data are difficult to obtain, while unlabeled data can be easily acquired. This paper focuses on an active semi-supervised algorithm that can be driven by multiple clustering hierarchies. If there is one or more hierarchies that can reasonably align clusters with class labels, then a few queries are needed to label with high quality all the unlabeled data. We take as a starting point the well-known Hierarchical Sampling (HS) algorithm and perform changes in different aspects of the original algorithm in order to tackle its main drawbacks, including its sensitivity to the choice of a single particular hierarchy. Experimental results over many real datasets show that the proposed algorithm performs superior or competitive when compared to a number of state-of-the-art algorithms for active semi-supervised classification.

Item ID:	46773
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-5090-5206-6
Keywords:	active learning; classification
Funders:	FAPESP, CNPq, Brazil, Natural Sciences and Engineering Research Council, Canada
Projects and Grants:	FAPESP 2014/01352-0, CNPq 304137/2013-8, CNPq 400772/2014-0
Date Deposited:	28 Mar 2017 23:39
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490599 Statistics not elsewhere classified @ 100%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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