Have a personal or library account? Click to login
On Improving the Classification of Imbalanced Data Cover

On Improving the Classification of Imbalanced Data

Open Access
|Apr 2017

References

  1. 1. Alcalá-Fdez, J., L. Sánchez, S. García, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernández, F. Herrera. KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. - Soft Computing, Vol. 13, 2009, No 3, pp. 307-318.10.1007/s00500-008-0323-y
  2. 2. Alcalá-Fdez, J., A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, F. Herrera. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. - Journal of Multiple-Valued Logic and Soft Computing, Vol. 17, 2011, No 2-3, pp. 255-287.
  3. 3. Barandela, R., J. S. Sanchez, V. Garcia, E. Rangel. Strategies for Learning in Class Imbalance Problems. - Pattern Recogn., Vol. 36, 2003, No 3, pp. 849-851.10.1016/S0031-3203(02)00257-1
  4. 4. Krawczyk, B. Learning from Imbalanced Data: Open Challenges and Future Directions. - Progress in Artificial Intelligence, Vol. 5, November 2016, No 4, pp. 221-232.10.1007/s13748-016-0094-0
  5. 5. Chawla, N. V., K.,W. Bowyer, L.,O. Hall, W. P. Kegelmeyer. SMOTE: Synthetic Minority Over-Sampling Technique. - J. Artificial Intelligence Research, Vol. 16, 2002, pp. 321-357.10.1613/jair.953
  6. 6. Chawla, N. V., N. Japkowicz, A. Kolcz. Editorial: Special Issue on Learning from Imbalanced Data Sets. - ACM SIGKDD Explorations Newsletter, Vol. 6, 2004, No 1, pp. 1-6.10.1145/1007730.1007733
  7. 7. Ramentol, E., S. Vluymans, N. Verbiest, Y. Caballero, R. Bello, C. Cornelis, F. Herrera. IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. - IEEE Transactions on Fuzzy Systems, Vol. 23, October 2015, No 5, pp. 1622-1637.10.1109/TFUZZ.2014.2371472
  8. 8. Frank, A., A. Asuncion. UCImachine learning repository. 2010. http://archive.ics.uci.edu/ml
  9. 9. Galar, M., A. Fernando, E. Barrenechea, H. Business, F. Herrera. A Review on Ensembles for the Class Imbalance Problem. - IEEE Transactions on Systems, Man, and Cybernetics. Part C: Applications and Review, Vol. 42, 2012.10.1109/TSMCC.2011.2161285
  10. 10. Garcia-Pedrajas, N., J. Perez-Rodriguez, M. Garcia-Pedrajas, D. Ortiz- Boyer, C. Fyfe. Class Imbalance Methods for Translation Initiation Site Recognition in DNA Sequences. - Knowl. Based Syst., Vol. 25, 2012, No 1, pp. 22-34.10.1016/j.knosys.2011.05.002
  11. 11. Guo, H., H. Viktor. Learning from Imbalanced Data Sets with Boosting and Data Generation: The Databoost-im Approach. - SIGKDD Explorations, Vol. 6, 2004, pp. 30-39.10.1145/1007730.1007736
  12. 12. Lee, J., D.-W. Kim. Mutual Information-Based Multi-Label Feature Selection Using Interaction Information. - Expert Systems with Applications, Vol. 42, March 2015, No 4, pp. 2013-2025.10.1016/j.eswa.2014.09.063
  13. 13. Jain, A., B. Chandrasekharan. Dimensionality and Sample Size Considerations in Pattern Recognition Practice. - In: P. Krishnaiah, L. Kanal, Eds. Handbook of Statistics. Vol. 2. North Holland, 1982, pp. 835-855.10.1016/S0169-7161(82)02042-2
  14. 14. Jo, T., N. Japkowicz. Class Imbalances Versus Small Disjuncts. - ACM SIGKDD, Vol. 6, 2004, No 1, pp. 40-4910.1145/1007730.1007737
  15. 15. Sáez, J. A., B. Krawczyk, M. Woźniak. Analyzing the Oversampling of Different Classes and Types of Examples in Multi-Class Imbalanced Datasets. - Pattern Recogn., Vol. 57, 2016, pp. 164-178.10.1016/j.patcog.2016.03.012
  16. 16. Peng, L., et al. Imbalanced Traffic Identification Using an Imbalanced Data Gravitation-Based Classification Model. - Computer Communications, 2016, pp. 347-373.
  17. 17. Moreno-Torres, J. G., F. Herrera. A Preliminary Study on Overlapping and Data Fracture in Imbalanced Domains by Means of Genetic Programming-Based Feature Extraction. - In: 10th International Conference on Intelligent Systems Design and Applications (ISDA’2010), 2010, pp. 501-506,10.1109/ISDA.2010.5687214
  18. 18. Moreno-Torres, J. G., T. Raeder, R. Alaíz-Rodríguez, N. V. Chawla, F. Herrera. A Unifying View on Dataset Shift in Classification. - Pattern Recogn., Vol. 45, 2012, No 1, pp. 521-530.10.1016/j.patcog.2011.06.019
  19. 19. Prati, R. C., G. E. A. P. A. Batista, M. C. Monard. Class Imbalances Versus Class Overlapping: An Analysis ofa Learning System Behavior. - In: R. Monroy, G. Arroyo- Figueroa, L. E. Sucar, H. Sossa, Eds. MICAI 2004. LNCS (LNAI). Vol. 2972. Heidelberg, Springer, 2004, pp. 312-321.
  20. 20. Yin, Q.-Y., J.-S. Zhang, C.-X. Zhang, N.-N. Ji. A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling. - In: Hindawi Publishing Corporation Mathematical Problems in Engineering. Vol. 2014. Article ID 358942, 14 p.10.1155/2014/358942
  21. 21. Satuluri, N., M. R. Kuppa. A Novel Class Imbalance Learning Using Intelligent Under- Sampling. - International Journal of Database Theory and Application, Vol. 5, 2012, pp. 25-35.
  22. 22. Seetha, H., R. Saravanan, M. N. Murty. Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification. - Cybernetics and Information Technologies, Vol. 12, 2012, No 4, pp. 77-94.10.2478/cait-2012-0032
  23. 23. Sun, Y., M. S. Kamel, A. K. C. Wong, Y. Wang. Cost-Sensitive Boosting for Classification of Imbalanced Data. - Pattern Recogn., Vol. 40, 2007, pp. 3358-3378.10.1016/j.patcog.2007.04.009
  24. 24. Tahira, M. A., J. Kittlera, F. Yan. Inverse Random under Sampling for Class Imbalance Problem and its Application to Multi-Label Classification. - Pattern Recogn., Vol. 45, 2012, No 10, pp. 3738-3750.10.1016/j.patcog.2012.03.014
  25. 25. López, V., A. Fernández, F. Herrera. On the Importance of the Validation Technique for Classification with Imbalanced Datasets: Addressing Covariate Shift when Data is Skewed. - Information Sciences, Vol. 257, February 2014, pp. 1-13.10.1016/j.ins.2013.09.038
  26. 26. Viswanath, P., M. N. Murty, S. Bhatnagar. Partition Based Pattern Synthesis Technique with Efficient Algorithms for Nearest Neighbor Classification. - Pattern Recognition Letters, Vol. 27, 2006, pp. 1714-1724.10.1016/j.patrec.2006.04.015
  27. 27. Wang, S., X. Yao. Multiclass Imbalance Problems: Analysis and Potential Solutions. - IEEE Trans. Syst., Man, Cybern. B, Vol. 42, 2012, No 4, pp. 1119-1130.10.1109/TSMCB.2012.218728022438514
  28. 28. Li, Y., X. Zhang. Improving k-Nearest Neighbour with Exemplar Generalization for Imbalanced Classification. Advances in Knowledge Discovery and Data Mining. - In: Lecture Notes in Computer Science. Vol. 6635. 2011, pp. 321-332.
  29. 29. Zhang, J., I. Mani. KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction. - In: Proc. of International Conf. Machine Learning (ICML’2003), Workshop Learning from Imbalanced Data Sets, 2003.
  30. 30. López, V., A. Fernández, S. García, V. Palade, F. Herrera. An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics. - In: Information Sciences. Vol. 250. Online Publication Date: 1 November 2013, pp. 113-141.10.1016/j.ins.2013.07.007
  31. 31. Borsos, Z., C. Lemnaru, R. Potolea. Dealing with Overlap and Imbalance: A New Metric and Approach. - Pattern Analysis and Applications. Online Publication Date: 27 September 2016.10.1007/s10044-016-0583-6
  32. 32. Zhang, Z., B. Krawczyk, S. Garcìa, A. Rosales-Pérez, F. Herrera. Empowering One-vs-One Decomposition with Ensemble Learning for Multi-Class Imbalanced Data. - Knowledge-Based Systems, Vol. 106, 2016, pp. 251-263.10.1016/j.knosys.2016.05.048
  33. 33. Zhou, Z.-H., X.-Y. Liu. On Multi-Class Cost-Sensitive Learning. - Comput. Intell., Vol. 26, 2010, No 3, pp. 232-257.10.1111/j.1467-8640.2010.00358.x
DOI: https://doi.org/10.1515/cait-2017-0004 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 45 - 62
Published on: Apr 6, 2017
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Lincy Meera Mathews, Hari Seetha, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.