Evaluating misclassifications in imbalanced data

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

Volumn 4212 LNAI, Issue , 2006, Pages 126-137

  Elazmeh, William ^a   Japkowicz, Nathalie ^a   Matwin, Stan ^a,b  

^a UNIVERSITY OF OTTAWA   (Canada)

^b INSTITUTE OF COMPUTER SCIENCE   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; PROBLEM SOLVING;

IMBALANCED DATA; ROC; TWO-CLASS PROBLEM;

LEARNING SYSTEMS;

EID: 33750356548     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_16     Document Type: Conference Paper

References (17)

1. Ling, C.X., Huang, J., Zang, H.: Auc: a better measure than accuracy in comparing learning algorithms. Canadian Conference on AI (2003) 329-341
2. Provost, F., Fawcett, T.: Analysis and visualization f classifier performance: Comparison under imprecise class and cost distributions, the Third International Conference on Knowledge Discovery and Data Mining (1997) 34-48
3. Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. Journal of Artificial Intelligence Research (10) (1999) 243-270
4. Swets, J.: Measuring the accuracy of diagnostic systems. Science (240) (1988) 1285-1293
5. Drummond, C., Holte, R.C.: Explicitly representing expected cost: An alternative to roc representation, the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2000) 198-207
6. Drummond, C., Holte, R.C.: What roc curves can't do (and cost curves can). ECAI'2004 Workshop on ROC Analysis in AI (2004)
7. Macskassy, S.A., Provost, F., Rosset, S.: Roc confidence bands: An empirical evaluation, in Proceedings of the 22nd International Conference on Machine Learning (ICML 2005) (2005) 537 - 544
8. Macskassy, S.A., Provost, F.: Confidence bands for roc curves: Methods and empirical study, in Proceedings of the 1st Workshop on ROC Analasis in AI (ROCAI-2004) at ECAI-2004 (2004)
9. Drummond, C., Holte, R.C.: Severe class imbalance: Why better algorithms aren't the answer. Proceedings of the 16th European Conference of Machine Learning (2005) 539-546
10. Motulsky, H.: Intuitive Biostatistics. Oxford University Press, New York (1995)
11. Tango, T.: Equivalence test and confidence interval for the difference in proportions for the paired-sample design. Statistics in Medicine 17 (1998) 891-908
12. Newcombe, R.G.: Improved confidence intervals for the difference between binomial proportions based on paired data. Statistics in Medicine 17 (1998) 2635-2650
13. Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html (1998) University of California, Irvine, Dept. of Information and Computer Sciences.
14. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. 2nd Edition, Morgan Kaufmann (2005)
15. Dietterich, T.G.: Approximate statistical test for comparing supervised classification learning algorithms. Neural Computation 10(7) (1998) 1895-1923
16. Newcombe, R.G.: Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine 17 (1998) 857-872
17. Everitt, B.S.: The analysis of contingency tables. Chapman-Hall (1992)