Active semi-supervised classification based on multiple clustering hierarchies

Antônio J.L. Batista, Ricardo J.G.B. Campello, Jörg Sander

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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.

OriginalsprogEngelsk
TitelProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
ForlagIEEE
Publikationsdato22. dec. 2016
Sider11-20
Artikelnummer7796886
ISBN (Elektronisk)9781509052066
DOI
StatusUdgivet - 22. dec. 2016
Udgivet eksterntJa
Begivenhed3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 - Montreal, Canada
Varighed: 17. okt. 201619. okt. 2016

Konference

Konference3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
Land/OmrådeCanada
ByMontreal
Periode17/10/201619/10/2016
SponsorIEEE Computational Intelligence Society

Bibliografisk note

Publisher Copyright:
© 2016 IEEE.

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