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|Title:||Handling of imbalanced data in text classification: Category-based term weights||Authors:||Liu, Y.
|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  and 2003  at AAAI and ICML conferences respectively and a special issue in ACM SIGKDD explorations  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 . © 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|
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