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
Source: 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
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