NDLI: Dimensionality reduction for active learning with nearest neighbour classifier in text categorisation problems

Content Provider	IEEE Xplore Digital Library
Author	Davy, M. Luz, S.
Copyright Year	2007
Description	Author affiliation: Trinity Coll. Dublin, Dublin (Davy, M.; Luz, S.)
Abstract	Dimensionality reduction techniques are commonly used in text categorisation problems to improve training and classification efficiency as well as to avoid overfitting. The best performing dimensionality reduction techniques for text categorisation are supervised, hence utilise the label information of the training data. Active learning is used to reduce the number of labelled training examples for problems where obtaining label information is expensive. Since the vast majority of data supplied to active learning are unlabelled, supervised dimensionality reduction techniques cannot be readily employed. For this reason, active learning in text categorisation problems do not perform dimensionality reduction thereby restricting the choice of classifier. In this paper we investigate unsupervised dimensionality reduction techniques in active learning for text categorisation problems. Two unsupervised techniques are investigated, namely document frequency and principal components analysis. We empirically show increased performance of active learning, using a k-nearest neighbour classifier, when dimensionality reduction is applied using the unsupervised techniques.
Starting Page	292
Ending Page	297
File Size	695384
Page Count	6
File Format	PDF
ISBN	9780769530697
DOI	10.1109/ICMLA.2007.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Text categorization Supervised learning Training data Machine learning Educational institutions Frequency Application software Artificial intelligence Principal component analysis
Content Type	Text
Resource Type	Article

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