Multi-class boosting for imbalanced data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9117, Issue , 2015, Pages 57-64

  Fernández Baldera, Antonio ^a   Buenaposada, José M ^b   Baumela, Luis ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSIDAD REY JUAN CARLOS   (Spain)

Author keywords

Boosting; Imbalanced data; Multi class classification

Indexed keywords

CLASSIFIERS; COST BENEFIT ANALYSIS; COSTS; IMAGE ANALYSIS; LEARNING SYSTEMS; MATRIX ALGEBRA; PATTERN RECOGNITION;

BOOSTING; BOOSTING ALGORITHM; CLASS IMBALANCE; CONFUSION MATRICES; COST-SENSITIVE; IMBALANCED DATA; IMBALANCED DATA-SETS; MULTI-CLASS CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84937401350     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-19390-8_7     Document Type: Conference Paper

References (12)

1. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263-1284 (2009)
2. Sun, Y., Kamel, M.S., Wang, Y.: Boosting for learning multiple classes with imbalanced class distribution. In: Proceedings of the International Conference on Data Mining. ICDM '06, pp. 592-602 (2006)
3. Masnadi-Shirazi, Hamed, Vasconcelos, N.: Cost-sensitive boosting. Trans. Pattern Anal. Mach. Intell. 33, 294-309 (2011)
4. Fan, W., Stolfo, S.J., Zhang, J., Chan, P.K.: Adacost: Misclassification costsensitive boosting. In: Proceedings of the 16th International Conference onMachine Learning, pp. 97-105 (1999)
5. Landesa-Vazquez, I., Alba-Castro, J.L.: Double-base asymmetric adaboost. Neurocomputing 118, 101-114 (2013)
6. Freund, Y., Schapire, R.E.: A decision theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 119-139 (1997)
7. Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337-407 (2000)
8. Zou, H., Zhu, J., Hastie, T.: New multicategory boosting algorithms based on multicategory fisher-consistent losses. Ann. Appl. Stat. 2, 1290-1306 (2008)
9. Zhu, J., Zou, H., Rosset, S., Hastie, T.: Multi-class AdaBoost. Stat. Interface 2, 349-360 (2009)
10. O'Brien, D.B., Gupta, M.R., Gray, R.M.: Cost-sensitive multi-class classification from probability estimates. In: Proceedings of the 25th International Conference on Machine Learning, pp. 712-719 (2008)
11. Fernandez-Baldera, A., Baumela, L.: Multi-class boosting with asymmetric weaklearners. Patt. Recogn. 47(5), 2080-2090 (2014)
12. Cetina, K., Márquez-Neila, P., Baumela, L.: A comparative study of feature descriptors for mitochondria and synapse segmentation. In: Proceedings of the International Conference on Pattern Recognition (2014)