Cost-sensitive neural network with ROC-based moving threshold for imbalanced classification

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

Volumn 9375 LNCS, Issue , 2015, Pages 45-52

  Krawczyk, Bartosz ^a   Woźniak, Michał ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Cost sensitive; Imbalanced classification; Machine learning; Moving threshold; Neural networks

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COST FUNCTIONS; COSTS; ENGINEERING EDUCATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS;

CLASSIFICATION ALGORITHM; COST-SENSITIVE; COST-SENSITIVE NEURAL NETWORKS; EXPERIMENTAL COMPARISON; IMBALANCED CLASSIFICATION; MOVING THRESHOLD; RECEIVER OPERATING CHARACTERISTIC CURVE ANALYSIS; STATE-OF-THE-ART METHODS;

COST BENEFIT ANALYSIS;

EID: 84983681031     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24834-9_6     Document Type: Conference Paper

References (13)

1. Antonelli, M., Ducange, P., Marcelloni, F.: An experimental study on evolutionary fuzzy classifiers designed for managing imbalanced datasets. Neurocomputing 146, 125–136 (2014)
2. Błaszczyński, J., Stefanowski, J.: Neighbourhood sampling in bagging for imbalanced data. Neurocomputing 150, 529–542 (2015)
3. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
4. Flach, P.A.: The geometry of ROC space: understanding machine learning metrics through ROC isometrics. In: Proceedings of the Twentieth International Conference on Machine Learning, ICML 2003, 21–24 August 2003,Washington, DC, USA, pp. 194–201 (2003)
5. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting- and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 42(4), 463–484 (2012)
6. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)
7. Krawczyk, B., Woźniak, M., Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification. Appl. Soft Comput. 14, 554–562 (2014)
8. Lopez, V., Fernandez, A., Moreno-Torres, J.G., Herrera, F.: Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification open problems on intrinsic data characteristics. Expert Syst. Appl. 39(7), 6585–6608 (2012)
9. Maloof, M.A.: Learning when data sets are imbalanced and when costs are unequal and unknown. In: ICML-2003 Workshop on Learning from Imbalanced Data Sets II (2003)
10. Provost, F.: Machine learning from imbalanced data sets 101. In: Proceedings of the AAAI 2000 Workshop on Imbalanced Data Sets, pp. 1–3 (2000)
11. Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: 1993 IEEE International Conference on Neural Networks, pp. 586–591 (1993)
12. Sun, Y., Wong, A.K.C., Kamel, M.S.: Classification of imbalanced data: a review. Int. J. Pattern Recogn. Artif. Intell. 23(4), 687–719 (2009)
13. Zhou, Z.-H., Liu, X.-Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63–77 (2006)