Class distribution estimation based on the Hellinger distance

Information Sciences

Volumn 218, Issue , 2013, Pages 146-164

  González Castro, Víctor ^a   Alaiz Rodríguez, Rocío ^a   Alegre, Enrique ^a  

^a UNIVERSITY OF LEÓN   (Spain)

Author keywords

Class distribution shift; Class prior probability estimation; Hellinger distance; Quantification

Indexed keywords

BASE CLASSIFIERS; BINARY CLASSIFICATION PROBLEMS; CLASS DISTRIBUTION ESTIMATION; CLASS DISTRIBUTIONS; CLASS LABELS; CONFUSION MATRICES; DIVERGENCE MEASURES; HELLINGER DISTANCE; HIGH VARIABILITY; MEAN ABSOLUTE ERROR; OPERATIONAL CONDITIONS; POSTERIOR PROBABILITY; PRIOR PROBABILITY; QUANTIFICATION; TEST DATA;

ESTIMATION;

PROBABILITY DISTRIBUTIONS;

EID: 84867404045     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.05.028     Document Type: Article

References (44)

1. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification 2001 John Wiley and Sons
2. L.L. Minku, A.P. White, and X. Yao The impact of diversity on online ensemble learning in the presence of concept drift IEEE Transactions on Knowledge and Data Engineering 22 5 2010 730 742
3. S. Gu, Y. Tan, X. He, Recentness biased learning for time series forecasting, Information Sciences, http://dx.doi.org/10.1016/j.ins.2010.09.004.
4. Y.S. Chan, H.T. Ng, Estimating class priors in domain adaptation for word sense disambiguation, in: ACL-44: Proc. of the 21st Int. Conf. on Computational Linguistics, 2006, pp. 89-96.
5. A. Guerrero-Curieses, R. Alaiz-Rodríguez, and J. Cid-Sueiro Cost-sensitive and modular land-cover classification based on posterior probability estimates International Journal of Remote Sensing 30 22 2009 5877 5899
6. C. Drummond, and R.C. Holte Cost curves: an improved method for visualizing classifier performance Machine Learning 65 1 2006 95 130
7. S. Vucetic, Z. Obradovic, Classification on data with biased class distribution, in: Proceedings of the 12th European Conference on Machine Learning (ECML, Freiburg), 2001, pp. 527-538.
8. M. Saerens, P. Latinne, and C. Decaestecker Adjusting a classifier for new a priori probabilities: a simple procedure Neural Computation 14 2002 21 41
9. J.C. Xue, G.M. Weiss, Quantification and semi-supervised classification methods for handling changes in class distribution, in: KDD '09: Proceedings of the 15th Int. Conf. on Knowledge Discovery and Data Mining, 2009, pp. 897-906.
10. R. Alaiz-Rodríguez, N. Japkowicz, Assessing the impact of changing environments on classifier performance, in: Proceedings of the Canadian Society for Computational Studies of Intelligence, 21st Conference on Advances in Artificial Intelligence, Canadian AI'08, Springer-Verlag, Berlin, Heidelberg, 2008, pp. 13-24.
11. R. Alaiz-Rodríguez, A. Guerrero-Curieses, and J. Cid-Sueiro Class and subclass probability re-estimation to adapt a classifier in the presence of concept drift Neurocomputing 74 16 2011 2614 2623
12. A. Fernández, M.J. del Jesus, and F. Herrera Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets International Journal of Approximate Reasoning 50 2009 561 577
13. A. Fernández, M.J. del Jesus, and F. Herrera On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets Information Sciences 180 8 2010 1268 1291
14. C. Seiffert, T.M. Khoshgoftaar, J.V. Hulse, A. Folleco, An empirical study of the classification performance of learners on imbalanced and noisy software quality data, Information Sciences, 2011, http://dx.doi.org/10.1016/j. ins.2010.12.016.
15. F. Provost, and T. Fawcett Robust classification systems for imprecise environments Machine Learning 42 3 2001 203 231
16. M.-C. Chen, L.-S. Chen, C.-C. Hsu, and W.-R. Zeng An information granulation based data mining approach for classifying imbalanced data Information Sciences 178 16 2008 3214 3227
17. J. Liu, Q. Hu, and D. Yu A weighted rough set based method developed for class imbalance learning Information Sciences 178 4 2008 1235 1256
18. M. Wang, X.-S. Hua, T. Mei, R. Hong, G. Qi, Y. Song, and L.-R. Dai Semi-supervised kernel density estimation for video annotation Computer Vision and Image Understanding 113 3 2009 384 396
19. H. He, and E.A. Garcia Learning from imbalanced data IEEE Transactions on Knowledge and Data Engineering 21 9 2009 1263 1284
20. R. Yanagimachi 2nd ed. Mammalian Fertilization vol. 1 1994 Raven Press
21. E. Alegre, V. González-Castro, S. Suárez, M. Castejón, Comparison of supervised and unsupervised methods to classify boar acrosomes using texture descriptors, in: Proc. Int. Symp. ELMAR '09, 2009, pp. 65-70.
22. R. Alaiz-Rodríguez, E. Alegre, V. González-Castro, L. Sánchez, Quantifying the proportion of damaged sperm cells based on image analysis and neural networks, in: Proc. of the 8th Conference on Simulation, Modelling and Optimization, 2008, pp. 383-388.
23. L. Sánchez, V. González, E. Alegre, R. Alaiz, Classification and quantification based on image analysis for sperm samples with uncertain damaged/intact cell proportions, in: Proc. of the 5th International Conf., ICIAR 2008, vol. 5112, Lecture Notes in Computer Science, 2008, pp. 827-836.
24. V. González-Castro, R. Alaiz-Rodríguez, L. Fernández-Robles, R. Guzmán-Martínez, E. Alegre, Estimating class proportions in boar semen analysis using the Hellinger Distance, in: Trends in Applied Intelligent Systems, vol. 6096, Lecture Notes in Computer Science, 2010, pp. 284-293.
25. G. Forman, Quantifying trends accurately despite classifier error and class imbalance, in: Principles and Practice of Knowledge Discovery in Databases, 2006, pp. 157-166.
26. G. Forman Quantifying counts and costs via classification Data Mining and Knowledge Discovery 17 2 2008 164 206
27. G. Forman, E. Kirshenbaum, J. Suermondt, Pragmatic text mining: minimizing human effort to quantify many issues in call logs, in: KDD '06: Proc. of the 12th Int. Conf. on Knowledge Discovery and Data Mining, 2006, pp. 852-861.
28. A. Bella, C. Ferri, J. Hernandez-Orallo, and M.J. Ramirez-Quintana Quantification via probability estimators IEEE International Conference on Data Mining 2010 737 742
29. I. Csiszar, P. Shields, Information Theory and Statistics: A Tutorial (Foundations and Trends in Communications and Information), Now Publishers Inc., 2004.
30. G. Forman, Counting positives accurately despite inaccurate classification, in: ECML, 2005, pp. 564-575.
31. C.M. Bishop Neural Networks for Pattern Recognition 1996 Oxford University Press
32. L. Tarassenko, and N.C.A. Forum A guide to neural computing applications 1998 A Hodder Arnold Publication Arnold
33. J.G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N.V. Chawla, and F. Herrera A unifying view on dataset shift in classification Pattern Recognition 45 1 2012 521 530
34. D.A. Cieslak, and N.V. Chawla A framework for monitoring classifiers performance: when and why failure occurs? Knowledge and Information Systems 18 1 2009 83 108
35. G. Ditzler, R. Polikar, Hellinger distance based drift detection for nonstationary environments, in: 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2011, pp. 41-48.
36. A. Frank, A. Asuncion, UCI Machine Learning Repository, 2010. < http://archive.ics.uci.edu/ml >.
37. V. González-Castro, E. Alegre, P. Morala-Argello, and S.A. Suarez A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform International Journal of Imaging 2 S09 2009 70 80
38. S. Arivazhagan, and L. Ganesan Texture classification using wavelet transform Pattern Recognition Letters 24 9-10 2003 1513 1521
39. M. González, E. Alegre, R. Alaiz, L. Sánchez, Acrosome integrity classification of boar spermatozoon images using dwt and texture techniques, in: VipIMAGE - Computational Vision and Medical Image Processing, 2007, pp. 165-168.
40. F. Wilcoxon Individual comparisons by ranking methods Biometrics 1 1945 80 83
41. J.H. Zar Biostatistical Analysis fifth ed. 2007 Prentice-Hall, Inc.
42. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
43. J.G. Moreno-Torres, X. Llorà, D.E. Goldberg, R. Bhargava, Repairing fractures between data using genetic programming-based feature extraction: a case study in cancer diagnosis, Information Sciences, http://dx.doi.org/10.1016/j.ins.2010.09.018.
44. M. Gebel, and C. Weihs Calibrating classifier scores into probabilities Advances in Data Analysis, Studies in Classification, Data Analysis, and Knowledge Organization 2007 Springer Berlin Heidelberg 141 148