A Study of Cleaning in Imbalanced Databases with Class Overlap
Abstract
This paper presents an undergoing study of the degeneration of classifiers resulting from skewed class distribution. Its central hypothesis is that the applying of regular sampling techniques may not improve classifiers’ performance probably because of the class overlap natural to the data or caused by the sampling techniques. A new approach based on non supervised learning is present for this problem. The results so far obtained from the method developed with this approach, denoted C-clear, are not conclusive yet, though it seems to be a promise approach.References
Batista, G.E.A.P.A.; Prati, R.C.; Monard, M.C. (2004) A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explorations, v.6 p.20-29.
Chawla, N.V.; Bowyer, K.W.; Hall, L.O. & Kegelmeyer, W.P. (2002) SMOTE: Synthetic Minority Over-sampling Technique. JAIR, v.16, p.321–357.
Chawla, N.V.; Japkowicz, N.; Kotcz, A. (2004) Editorial: Special Issue on Learning from Imbalanced Data Sets. ACM SIGKDD Explorations. v.6. p.1-6.
Fawcett, T. (2004) “ROC Graphs Notes and Practical Considerations”, Machine Learning.
Ferri, C.; Flach, P.; Hernández-Orallo, J. H. (2002) Learning Decision Trees using the Area under the ROC curve. In C. S. A. Hoffman, editor, Nineteenth International Conference on Machine Learning (ICML). Morgan Kaufmann Publishers. p.139–146.
Guo, H.; Viktor, H.L. (2004) Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach. SIGKDD Explorations, v6 p.30-39
He, Z; Xu, X.; Deng, S.; (2002) Squeezer: an Efficient Algorithm for Clustering Categorical Data. Jornal of Computer Science and Technology. v.17, n.5, p.611-625
He, Z; Xu, X.; Deng, S.; (2005) Clustering Mixed Numeric and Categorical Data: a Cluster Ensemble Approach. ArXiv Computer Science e-prints. (Acesso em 12/12/2006. Disponível em: [link]
Han, H.; Wang, W.Y.; Mao, B.H. (2005) Borderline-SMOTE: a New Over-Sampling Method in Imbalanced Data Sets Learning. Advances in Intelligent Computing. International Conference on Intelligent Computing (ICIC). Lecture Notes in Computer Science. V.3644, Springer-Verlag, Hefei (China) p.878-887
Japkowicz, N. (2002) Supervised Learning with Unsupervised Output Separation. In Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing (ASC). p.321-325.
Japkowicz, N. (2003) Class imbalances: Are we Focusing on the Right Issue? In Proceedings of the ICML Workshop on Learning from Imbalanced Data Sets (II).
Jo, T.; Japkowicz, N. (2004). Class Imbalances versus Small Disjuncts. SIGKDD Explorations, v.6, p.40-49.
Kohavi, R. (1995) A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. International Joint Conference on Artificial Intelligence (IJCAI). p.1137-1145
Ladeira, M; Vieira, M.H.P; Prado, H.A; Noivo, R.M; Castanheira, D.B.S (2005). UnBMiner Ferramenta Aberta Para Mineração de Dados. Revista Tecnologia da Informação, Brasília-DF, v.5, n.1, p.45-63.
MacQueen, J.B. (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, University of California Press, v.1, p.281-297.
Merz, C.J.; Murphy, P.M. (1998) UCI Repository of Machine Learning Datasets. [link]. (Acesso em 20/01/2007).
Nickerson, A.; Japkowicz, N.; Milios, E. (2001) Using Unsupervised Learning to Guide Re-Sampling in Imbalanced Data Sets. Proceedings of the Eighth International Workshop on AI and Statistics. p. 261-265.
Phua, C.; Alahakoon, D.; Lee, V. (2004). Minority Report in Fraud Detection: Classification of Skewed Data. ACM SIGKDD Explorations. v.6, p.50-59.
Prati, R.C.; Batista, G.E.A.P.A.; Monard, M.C (2004). Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior. In MICAI, p. 312-321.
Quinlan, J.R. (1993). C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
Sanches, M. K.; Monard, M. C. (2004). Proposta de um Algoritmo de Clustering Semisupervisionado para Rotular Exemplos a Partir de Poucos Exemplos Rotulados. In: Workshop in Artificial Intelligence, Arica-Chile. Jornadas Chilenas de Computación. Chile : Sociedad Chilena de Ciencias de la Computación, v.1, p.1-9.
SPSS Inc., NCR Systems Engineering Copenhagen & DaimlerChrysler AG (1999). CRISP-DM 1.0 – Step-by-step Data Mining Guide. SPSS & CRISP-DM Consortium. (Acesso em 05/03/2005. Disponível em [link].
Wilson, D.R.; Martinez, T.R. (2000). Reduction Techniques for Exemplar-Based Learning Algorithms. Machine Learning. v.38, n.3, p 257-286.
Weiss, G. (2004) Mining with Rarity: A Unifying Framework. ACM SIGKDD Explorations v.6. p.7-19.
Chawla, N.V.; Bowyer, K.W.; Hall, L.O. & Kegelmeyer, W.P. (2002) SMOTE: Synthetic Minority Over-sampling Technique. JAIR, v.16, p.321–357.
Chawla, N.V.; Japkowicz, N.; Kotcz, A. (2004) Editorial: Special Issue on Learning from Imbalanced Data Sets. ACM SIGKDD Explorations. v.6. p.1-6.
Fawcett, T. (2004) “ROC Graphs Notes and Practical Considerations”, Machine Learning.
Ferri, C.; Flach, P.; Hernández-Orallo, J. H. (2002) Learning Decision Trees using the Area under the ROC curve. In C. S. A. Hoffman, editor, Nineteenth International Conference on Machine Learning (ICML). Morgan Kaufmann Publishers. p.139–146.
Guo, H.; Viktor, H.L. (2004) Learning from Imbalanced Data Sets with Boosting and Data Generation: The DataBoost-IM Approach. SIGKDD Explorations, v6 p.30-39
He, Z; Xu, X.; Deng, S.; (2002) Squeezer: an Efficient Algorithm for Clustering Categorical Data. Jornal of Computer Science and Technology. v.17, n.5, p.611-625
He, Z; Xu, X.; Deng, S.; (2005) Clustering Mixed Numeric and Categorical Data: a Cluster Ensemble Approach. ArXiv Computer Science e-prints. (Acesso em 12/12/2006. Disponível em: [link]
Han, H.; Wang, W.Y.; Mao, B.H. (2005) Borderline-SMOTE: a New Over-Sampling Method in Imbalanced Data Sets Learning. Advances in Intelligent Computing. International Conference on Intelligent Computing (ICIC). Lecture Notes in Computer Science. V.3644, Springer-Verlag, Hefei (China) p.878-887
Japkowicz, N. (2002) Supervised Learning with Unsupervised Output Separation. In Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing (ASC). p.321-325.
Japkowicz, N. (2003) Class imbalances: Are we Focusing on the Right Issue? In Proceedings of the ICML Workshop on Learning from Imbalanced Data Sets (II).
Jo, T.; Japkowicz, N. (2004). Class Imbalances versus Small Disjuncts. SIGKDD Explorations, v.6, p.40-49.
Kohavi, R. (1995) A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. International Joint Conference on Artificial Intelligence (IJCAI). p.1137-1145
Ladeira, M; Vieira, M.H.P; Prado, H.A; Noivo, R.M; Castanheira, D.B.S (2005). UnBMiner Ferramenta Aberta Para Mineração de Dados. Revista Tecnologia da Informação, Brasília-DF, v.5, n.1, p.45-63.
MacQueen, J.B. (1967) Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, University of California Press, v.1, p.281-297.
Merz, C.J.; Murphy, P.M. (1998) UCI Repository of Machine Learning Datasets. [link]. (Acesso em 20/01/2007).
Nickerson, A.; Japkowicz, N.; Milios, E. (2001) Using Unsupervised Learning to Guide Re-Sampling in Imbalanced Data Sets. Proceedings of the Eighth International Workshop on AI and Statistics. p. 261-265.
Phua, C.; Alahakoon, D.; Lee, V. (2004). Minority Report in Fraud Detection: Classification of Skewed Data. ACM SIGKDD Explorations. v.6, p.50-59.
Prati, R.C.; Batista, G.E.A.P.A.; Monard, M.C (2004). Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior. In MICAI, p. 312-321.
Quinlan, J.R. (1993). C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
Sanches, M. K.; Monard, M. C. (2004). Proposta de um Algoritmo de Clustering Semisupervisionado para Rotular Exemplos a Partir de Poucos Exemplos Rotulados. In: Workshop in Artificial Intelligence, Arica-Chile. Jornadas Chilenas de Computación. Chile : Sociedad Chilena de Ciencias de la Computación, v.1, p.1-9.
SPSS Inc., NCR Systems Engineering Copenhagen & DaimlerChrysler AG (1999). CRISP-DM 1.0 – Step-by-step Data Mining Guide. SPSS & CRISP-DM Consortium. (Acesso em 05/03/2005. Disponível em [link].
Wilson, D.R.; Martinez, T.R. (2000). Reduction Techniques for Exemplar-Based Learning Algorithms. Machine Learning. v.38, n.3, p 257-286.
Weiss, G. (2004) Mining with Rarity: A Unifying Framework. ACM SIGKDD Explorations v.6. p.7-19.
Published
2007-06-30
How to Cite
MACHADO, Emerson Lopes; LADEIRA, Marcelo.
A Study of Cleaning in Imbalanced Databases with Class Overlap. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 6. , 2007, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2007
.
p. 1499-1508.
ISSN 2763-9061.
