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

Michael Davy*, Saturnino Luz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Pages292-297
Number of pages6
DOIs
Publication statusPublished - 25 Feb 2008
Event6th International Conference on Machine Learning and Applications, ICMLA 2007 - Cincinnati, OH, United States
Duration: 13 Dec 200715 Dec 2007

Publication series

NameProceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Conference

Conference6th International Conference on Machine Learning and Applications, ICMLA 2007
CountryUnited States
CityCincinnati, OH
Period13/12/0715/12/07

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