Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-1-84628-754-1_10
Title: Handling of imbalanced data in text classification: Category-based term weights
Authors: Liu, Y. 
Loh, H.T. 
Kamal, Y.-T.
Tor, S.B.
Issue Date: 2007
Citation: Liu, Y.,Loh, H.T.,Kamal, Y.-T.,Tor, S.B. (2007). Handling of imbalanced data in text classification: Category-based term weights. Natural Language Processing and Text Mining : 171-192. ScholarBank@NUS Repository. https://doi.org/10.1007/978-1-84628-754-1_10
Abstract: Learning from imbalanced data has emerged as a new challenge to the machine learning (ML), data mining (DM) and text mining (TM) communities. Two recent workshops in 2000 [17] and 2003 [7] at AAAI and ICML conferences respectively and a special issue in ACM SIGKDD explorations [8] are dedicated to this topic. It has been witnessing growing interest and attention among researchers and practitioners seeking solutions in handling imbalanced data. An excellent review of the state-ofthe- art is given by Gary Weiss [43]. © 2007 Springer-Verlag London Limited.
Source Title: Natural Language Processing and Text Mining
URI: http://scholarbank.nus.edu.sg/handle/10635/67957
ISBN: 184628175X
DOI: 10.1007/978-1-84628-754-1_10
Appears in Collections:Staff Publications

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

SCOPUSTM   
Citations

3
checked on Oct 12, 2019

Page view(s)

81
checked on Oct 13, 2019

Google ScholarTM

Check

Altmetric


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