Data Mining on Imbalanced Data Sets

The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced, and implicitly assume that all misclassification errors cost equally. But data in real-world is usually imbalanced. The class imbalance problem is pervasive and ubiquitous, causing trouble to a large segment of the data mining community. The tradition machine learning algorithms have bad performance when they learn from imbalanced data sets. Thus, machine learning on imbalanced data sets becomes an urgent problem. The importance of imbalanced data sets and their broad application domains in data mining are introduced, and then methods to deal with the class imbalance problem are discussed and their effectiveness are compared. Last but not least, the existing evaluation measures of class imbalance problem are systematically analyzed.

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

[2]  Gary M. Weiss.  Mining with rarity: a unifying framework , 2004, SKDD.

[3]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[4]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[5]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[6]  Nathalie Japkowicz,et al.  Supervised Versus Unsupervised Binary-Learning by Feedforward Neural Networks , 2004, Machine Learning.

[7]  Moninder Singh,et al.  Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management , 1996, ICML.

[8]  Edward Y. Chang,et al.  Class-Boundary Alignment for Imbalanced Dataset Learning , 2003 .

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[10]  I. Tomek,et al.  Two Modifications of CNN , 1976 .

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

[12]  Foster Provost,et al.  Machine Learning from Imbalanced Data Sets 101 , 2008 .

[13]  Tom Fawcett,et al.  Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.

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

[15]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[16]  Bianca Zadrozny,et al.  Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.

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

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

[19]  Tom Fawcett,et al.  Combining Data Mining and Machine Learning for Effective User Profiling , 1996, KDD.

[20]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[21]  Yi Lin,et al.  Support Vector Machines for Classification in Nonstandard Situations , 2002, Machine Learning.

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

[23]  Vipin Kumar,et al.  Evaluating boosting algorithms to classify rare classes: comparison and improvements , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

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

[25]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[26]  Malik Yousef,et al.  One-Class SVMs for Document Classification , 2002, J. Mach. Learn. Res..

[27]  Jorma Laurikkala,et al.  Improving Identification of Difficult Small Classes by Balancing Class Distribution , 2001, AIME.