Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

The class imbalanced problem occurs in various disciplines when one of target classes has a tiny number of instances comparing to other classes. A typical classifier normally ignores or neglects to detect a minority class due to the small number of class instances. SMOTE is one of over-sampling techniques that remedies this situation. It generates minority instances within the overlapping regions. However, SMOTE randomly synthesizes the minority instances along a line joining a minority instance and its selected nearest neighbours, ignoring nearby majority instances. Our technique called Safe-Level-SMOTE carefully samples minority instances along the same line with different weight degree, called safe level. The safe level computes by using nearest neighbour minority instances. By synthesizing the minority instances more around larger safe level, we achieve a better accuracy performance than SMOTE and Borderline-SMOTE.

[1]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[2]  Stan Matwin,et al.  Machine Learning for the Detection of Oil Spills in Satellite Radar Images , 1998, Machine Learning.

[3]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[4]  Nitesh V. Chawla,et al.  SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS , 2004 .

[5]  Andrew P. Bradley,et al.  The use of the area under the ROC curve in the evaluation of machine learning algorithms , 1997, Pattern Recognit..

[6]  Gregg D. Wilensky,et al.  Neural Network Studies , 1993 .

[7]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[8]  Fredric C. Gey,et al.  The relationship between recall and precision , 1994 .

[9]  Luis Enrique Sucar,et al.  MICAI 2004: Advances in Artificial Intelligence , 2004, Lecture Notes in Computer Science.

[10]  Gustavo E. A. P. A. Batista,et al.  Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior , 2004, MICAI.

[11]  Graham J. Williams,et al.  Data Mining , 2000, Communications in Computer and Information Science.

[12]  N. Bodor,et al.  Neural network studies: Part 3. Prediction of partition coefficients , 1994 .

[13]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[15]  Michael J. Pazzani,et al.  Reducing Misclassification Costs , 1994, ICML.

[16]  Nitesh V. Chawla,et al.  Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.

[17]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[18]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[19]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[20]  Xiao-Ping Zhang,et al.  Advances in Intelligent Computing, International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I , 2005, ICIC.

[21]  Salvatore J. Stolfo,et al.  Using artificial anomalies to detect unknown and known network intrusions , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[22]  Salvatore J. Stolfo,et al.  AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.

[23]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[24]  David D. Lewis,et al.  Heterogeneous Uncertainty Sampling for Supervised Learning , 1994, ICML.

[25]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[26]  Nathalie Japkowicz,et al.  The Class Imbalance Problem: Significance and Strategies , 2000 .

[27]  Igor V. Tetko,et al.  Neural network studies, 1. Comparison of overfitting and overtraining , 1995, J. Chem. Inf. Comput. Sci..

[28]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.