Theoretical study of the relationship between diversity and single-class measures for class imbalance learning

ICDM Workshops 2009 - IEEE International Conference on Data Mining

Volumn , Issue , 2009, Pages 76-81

  Wang, Shuo ^a   Yao, Xin ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Diversity; Ensemble learning; Imbalanced data; Single class performance measure

Indexed keywords

CLASS IMBALANCE LEARNING; CLASSIFICATION PERFORMANCE; ENSEMBLE LEARNING; FRAUD DETECTION; IMBALANCED CLASS; IMBALANCED DATA; MEDICAL DIAGNOSIS; PERFORMANCE MEASURE; REAL-WORLD PROBLEM; THEORETICAL RESEARCH; THEORETICAL STUDY;

DIAGNOSIS; MEDICAL PROBLEMS; TECHNICAL PRESENTATIONS;

CONDITION MONITORING;

EID: 77951201336     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2009.29     Document Type: Conference Paper

References (14)

1. N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, "Smoteboost: Improving prediction of the minority class in boosting, " in Knowledge Discovery in Databases: PKDD 2003, vol. 2838/2003, 2003, pp. 107-119.
2. H. Guo and H. L. Viktor, "Learning from imbalanced data sets with boosting and data generation: the databoost-im approach, " SIGKDD Explor. Newsl., vol. 6, no. 1, pp. 30-39, 2004.
3. W. Fan, S. J. Stolfo, J. Zhang, and P. K. Chan, "Adacost: Misclassification cost-sensitive boosting, " in Proc. 16th International Conf. on Machine Learning, 1999, pp. 97-105.
4. G. Brown, J. L. Wyatt, and P. Tino, "Managing diversity in regression ensembles, " Journal of Machine Learning Research, vol. 6, pp. 1621-1650, 2005.
5. K. Tumer and J. Ghosh, "Linear and order statistics combiners for pattern classification, " Combining Artificial Neural Networks, pp. 127-162, 1999.
6. L. Kuncheva, C. Whitaker, C. Shipp, and R. Duin, "Limits on the majority vote accuracy in classifier fusion, " Pattern Analysis and Applications, vol. 6, no. 1, pp. 22-31, 2003.
7. S. Kotsiantis, D. Kanellopoulos, and P. Pintelas, "Handling imbalanced datasets: A review, " GESTS International Transactions on Computer Science and Engineering, vol. 30, no. 1, pp. 25-36, 2006.
8. N. V. Chawla and J. Sylvester, "Exploiting diversity in ensembles: Improving the performance on unbalanced datasets, " Multiple Classifier Systems, vol. 4472, pp. 397-406, 2007.
9. C. V. Rijsbergen, Information Retrieval. London, Butterworths, 1979.
10. S. Wang and X. Yao, "Diversity analysis on imbalanced data sets by using ensemble models, " in IEEE Symposium on Computational Intelligence and Data Mining 2009, 2009, pp. 324-331.
11. S. Wang, K. Tang, and X. Yao, "Diversity exploration and negative correlation learning on imbalanced data sets, " in International Joint Conference on Neural Networks 2009, 2009, pp. 3259-3266.
12. G. U. Yule, "On the association of attributes in statistics, " Philosophical Transactions of the Royal Society of London, vol. A194, pp. 257-319, 1900.
13. L. Kuncheva and C. Whitaker, "Ten measures of diversity in classifier ensembles: limits for two classifiers, " in A DERA/IEE Workshop on Intelligent Sensor Processing (Ref. No. 2001/050), 2001, pp. 1-10.
14. L. I. Kuncheva, "That elusive diversity in classifier ensembles, " Pattern Recognition and Image Analysis, vol. 2652, pp. 1126-1138, 2003.