Robustness of Learning Techniques in Handling Class Noise in Imbalanced Datasets. Anyfantis, D., Karagiannopoulos, M., Kotsiantis, S., & Pintelas, P. Volume 247. Artificial Intelligence and Innovations 2007: from Theory to Applications, pages 21--28. Springer US, New York, NY, 2007.
doi  abstract   bibtex   
Many real world datasets exhibit skewed class distributions in which almost all instances are allotted to a class and far fewer instances to a smaller, but more interesting class. A classifier induced from an imbalanced dataset has a low error rate for the majority class and an undesirable error rate for the minority class. Many research efforts have been made to deal with class noise but none of them was designed for imbalanced datasets. This paper provides a study on the various methodologies that have tried to handle the imbalanced datasets and examines their robustness in class noise.
@inbook{Anyfantis:2007aa,
	Abstract = {Many real world datasets exhibit skewed class distributions in which almost all instances are allotted to a class and far fewer instances to a smaller, but more interesting class. A classifier induced from an imbalanced dataset has a low error rate for the majority class and an undesirable error rate for the minority class. Many research efforts have been made to deal with class noise but none of them was designed for imbalanced datasets. This paper provides a study on the various methodologies that have tried to handle the imbalanced datasets and examines their robustness in class noise.},
	Address = {New York, NY},
	Author = {Anyfantis, D. and Karagiannopoulos, M. and Kotsiantis, S. and Pintelas, P.},
	Chapter = {Robustness of Learning Techniques in Handling Class Noise in Imbalanced Datasets},
	Date-Added = {2008-08-05 21:55:43 -0400},
	Date-Modified = {2008-08-05 22:01:14 -0400},
	Doi = {10.1007/978-0-387-74161-1_3},
	Keywords = {machine learning; data mining; classification; naive bayes; noise},
	Pages = {21--28},
	Publisher = {Springer US},
	Series = {{IFIP} International Federation for Information Processing},
	Title = {Artificial Intelligence and Innovations 2007: from Theory to Applications},
	Volume = {247},
	Year = {2007},
	Bdsk-File-1 = {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}}

Downloads: 0