Handling of imbalanced data in text classification: category-based term weights

Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Loh, Han Tong, Kamal, Youcef-Toumi and Tor, Shu Beng 2007. Handling of imbalanced data in text classification: category-based term weights. Kao, Anne and Poteet, Stephen R., eds. Natural Language Processing and Text Mining, London: Springer, pp. 171-192. (10.1007/978-1-84628-754-1_10)

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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].

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Springer
ISBN:	9781846281754
Last Modified:	25 Oct 2022 08:03
URI:	https://orca.cardiff.ac.uk/id/eprint/51240

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