NDLI: Boosting decision stumps to do pairwise classification

Content Provider	IET Digital Library
Author	Jun, Xie Lu, Yu Lei, Zhu Hui, Xue
Abstract	Pairwise classification is a task which predicts whether two samples belong to the same class or not. Boosting provides a way of combining many weak classifiers to produce a strong one and has been regarded as one of the most successful classification methodologies. The problem of pairwise classification is addressed by boosting decision stumps, the simplest weak classifier. Based on gentle AdaBoost, pairwise gentle AdaBoost of decision stumps is proposed to do pairwise classification. To make the classifier deal with a pair of inputs, sample-weighted linear discriminant analysis (LDA) is proposed, which is tailored to boosting the framework. For pairwise classification, the proposed algorithm shows better performance than traditional boosting of decision stumps on two UCI data sets.
Starting Page	866
Ending Page	868
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 12, Jun (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.0128
Journal	Electronics Letters
Publisher Date	2014-05-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Boosting Decision Stumps Data Handling Data Handling Technique Gentle AdaBoost LDA Learning in AI Pairwise Classification Pattern Classification Sample Weighted Linear Discriminant Analysis UCI Data Sets
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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