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.
CITATION STYLE
Liu, Y., Loh, H. T., Kamal, Y. T., & Tor, S. B. (2007). Handling of imbalanced data in text classification: Category-based term weights. In Natural Language Processing and Text Mining (pp. 171–192). Springer London. https://doi.org/10.1007/978-1-84628-754-1_10
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