Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/42622
DC FieldValue
dc.titleA comparative study of centroid-based, neighborhood-based and statistical approaches for effective document categorization
dc.contributor.authorTam, V.
dc.contributor.authorSantoso, A.
dc.contributor.authorSetiono, R.
dc.date.accessioned2013-07-11T10:13:56Z
dc.date.available2013-07-11T10:13:56Z
dc.date.issued2002
dc.identifier.citationTam, V.,Santoso, A.,Setiono, R. (2002). A comparative study of centroid-based, neighborhood-based and statistical approaches for effective document categorization. Proceedings - International Conference on Pattern Recognition 16 (4) : 235-238. ScholarBank@NUS Repository.
dc.identifier.issn10514651
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42622
dc.description.abstractAssociating documents to relevant categories is critical for effective document retrieval. Here, we compare the well-known k-Nearest Neighborhood (kNN) algorithm, the centroid-based classifier and the Highest Average Similarity over Retrieved Documents (HASRD) algorithm, for effective document categorization. We use various measures such as the micro and macro F1 values to evaluate their performance on the Reuters-21578 corpus. The empirical results show that kNN performs the best, followed by our adapted HASRD and the centroid-based classifier for common document categories, while the centroid-based classifier and kNN outperform our adapted HASRD for rare document categories. Additionally, our study clearly indicates that each classifier performs optimally only when a suitable term weighting scheme is used. All these significant results lead to many exciting directions for future exploration. © 2002 IEEE.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.sourcetitleProceedings - International Conference on Pattern Recognition
dc.description.volume16
dc.description.issue4
dc.description.page235-238
dc.description.codenPICRE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.