Inverse random under sampling for class imbalance problem and its application to multi-label classification. (English)
Pattern Recognition 45, No. 10, 3738-3750 (2012).
1
Resampling methods versus cost functions for training an MLP in the class imbalance context. (English)
Liu, Derong (ed.) et al., Advances in neural networks ‒ ISNN 2011. 8th international symposium on neural networks, ISNN 2011, Guilin, China, May 29 ‒ June 1, 2011. Proceedings, Part II. Berlin: Springer (ISBN 978-3-642-21089-1/pbk). Lecture Notes in Computer Science 6676, 19-26 (2011).
2
On the suitability of combining feature selection and resampling to manage data complexity. (English)
Meseguer, Pedro (ed.) et al., Current topics in artificial intelligence. 13th conference of the Spanish association for artificial intelligence, CAEPIA 2009, Seville, Spain, November 9‒13, 2009. Selected papers. Berlin: Springer (ISBN 978-3-642-14263-5/pbk). Lecture Notes in Computer Science 5988. Lecture Notes in Artificial Intelligence, 141-150 (2010).
3
Classification of imbalanced data: a review. (English)
Int. J. Pattern Recognit. Artif. Intell. 23, No. 4, 687-719 (2009).
4
Melodic track identification in MIDI files considering the imbalanced context. (English)
Araujo, Helder (ed.) et al., Pattern recognition and image analysis. 4th Iberian conference, IbPRIA 2009, Póvoa de Varzim, Portugal, June 10‒12, 2009. Proceedings. Berlin: Springer (ISBN 978-3-642-02171-8/pbk). Lecture Notes in Computer Science 5524, 489-496 (2009).
5
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. (English)
Fuzzy Sets Syst. 159, No. 18, 2378-2398 (2008).
6
An evaluation of the robustness of MTS for imbalanced data. (English)
IEEE Trans. Knowl. Data Eng. 19, No. 10, 1321-1332 (2007).
7
Cost-sensitive boosting for classification of imbalanced data. (English)
Pattern Recognition 40, No. 12, 3358-3378 (2007).
8
Start
Contact
Print
General Help
