Imbalance class problems in data mining: a review

The imbalanced data problems in data mining are common nowadays, which occur due to skewed nature of data. These problems impact the classification process negatively in machine learning process. In such problems, classes have different ratios of specimens in which a large number of specimens belong to one class and the other class has fewer specimens that is usually an essential class, but unfortunately misclassified by many classifiers. So far, significant research is performed to address the imbalanced data problems by implementing different techniques and approaches. In this research, a comprehensive survey is performed to identify the challenges of handling imbalanced class problems during classification process using machine learning algorithms. We discuss the issues of classifiers which endorse bias for majority class and ignore the minority class. Furthermore, the viable solutions and potential future directions are provided to handle the problems .

[1]  Xin Yao,et al.  Diversity analysis on imbalanced data sets by using ensemble models , 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

[2]  Jing Zhao,et al.  ACOSampling: An ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data , 2013, Neurocomputing.

[3]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[4]  Taghi M. Khoshgoftaar,et al.  Feature Selection with High-Dimensional Imbalanced Data , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[5]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[6]  Pavel Brazdil,et al.  Cost-Sensitive Decision Trees Applied to Medical Data , 2007, DaWaK.

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

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

[9]  Kai Petersen,et al.  Systematic Mapping Studies in Software Engineering , 2008, EASE.

[10]  Szymon Wilk,et al.  Selective Pre-processing of Imbalanced Data for Improving Classification Performance , 2008, DaWaK.

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

[12]  Robert B. Fisher,et al.  Classifying imbalanced data sets using similarity based hierarchical decomposition , 2015, Pattern Recognit..

[13]  Taghi M. Khoshgoftaar,et al.  Knowledge discovery from imbalanced and noisy data , 2009, Data Knowl. Eng..

[14]  Annarita D'Addabbo,et al.  Parallel selective sampling method for imbalanced and large data classification , 2015, Pattern Recognit. Lett..

[15]  Robert E. Schapire,et al.  The Strength of Weak Learnability (Extended Abstract) , 1989, FOCS 1989.

[16]  Kannan Govindan,et al.  ELECTRE: A comprehensive literature review on methodologies and applications , 2016, Eur. J. Oper. Res..

[17]  Yuming Zhou,et al.  A novel ensemble method for classifying imbalanced data , 2015, Pattern Recognit..

[18]  M. A. H. Farquad,et al.  Preprocessing unbalanced data using support vector machine , 2012, Decis. Support Syst..

[19]  María José del Jesús,et al.  On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets , 2010, Inf. Sci..

[20]  M. Ali Fauzi,et al.  Neighbor Weighted K-Nearest Neighbor for Sambat Online Classification , 2018, Indonesian Journal of Electrical Engineering and Computer Science.

[21]  Seetha Hari,et al.  Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.

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

[23]  Adam Kowalczyk,et al.  Extreme re-balancing for SVMs: a case study , 2004, SKDD.

[24]  Jerzy Stefanowski,et al.  Neighbourhood sampling in bagging for imbalanced data , 2015, Neurocomputing.

[25]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[26]  Josef Kittler,et al.  Inverse random under sampling for class imbalance problem and its application to multi-label classification , 2012, Pattern Recognit..

[27]  David A. Cieslak,et al.  Automatically countering imbalance and its empirical relationship to cost , 2008, Data Mining and Knowledge Discovery.

[28]  Rohini K. Srihari,et al.  Feature selection for text categorization on imbalanced data , 2004, SKDD.

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

[30]  Anna Saro Vijendran,et al.  Adaptive Data Structure Based Oversampling Algorithm for Ordinal Classification , 2018, Indonesian Journal of Electrical Engineering and Computer Science.

[31]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[32]  Xue-wen Chen,et al.  Combating the Small Sample Class Imbalance Problem Using Feature Selection , 2010, IEEE Transactions on Knowledge and Data Engineering.

[33]  Pearl Brereton,et al.  Lessons from applying the systematic literature review process within the software engineering domain , 2007, J. Syst. Softw..

[34]  Swagatam Das,et al.  Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs , 2015, Neural Networks.

[35]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[36]  Ying He,et al.  MSMOTE: Improving Classification Performance When Training Data is Imbalanced , 2009, 2009 Second International Workshop on Computer Science and Engineering.

[37]  Kun-Huang Chen,et al.  A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients , 2014, Appl. Soft Comput..

[38]  Yang Wang,et al.  Boosting for Learning Multiple Classes with Imbalanced Class Distribution , 2006, Sixth International Conference on Data Mining (ICDM'06).

[39]  Robert Tibshirani,et al.  Classification by Pairwise Coupling , 1997, NIPS.

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

[41]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[42]  Alfredo Petrosino,et al.  Adjusted F-measure and kernel scaling for imbalanced data learning , 2014, Inf. Sci..

[43]  Victor S. Sheng,et al.  Cost-Sensitive Learning and the Class Imbalance Problem , 2008 .

[44]  Yves Deville,et al.  Multi-class protein fold classification using a new ensemble machine learning approach. , 2003, Genome informatics. International Conference on Genome Informatics.

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

[46]  Edward Y. Chang,et al.  KBA: kernel boundary alignment considering imbalanced data distribution , 2005, IEEE Transactions on Knowledge and Data Engineering.

[47]  Nitesh V. Chawla,et al.  SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.

[48]  Pearl Brereton,et al.  Performing systematic literature reviews in software engineering , 2006, ICSE.

[49]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[50]  Francisco Herrera,et al.  SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering , 2015, Inf. Sci..

[51]  Rosa Maria Valdovinos,et al.  New Applications of Ensembles of Classifiers , 2003, Pattern Analysis & Applications.

[52]  Xin Yao,et al.  MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning , 2014 .

[53]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

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

[55]  Qiang Yang,et al.  Test strategies for cost-sensitive decision trees , 2006, IEEE Transactions on Knowledge and Data Engineering.

[56]  Taghi M. Khoshgoftaar,et al.  A Comparative Study of Data Sampling and Cost Sensitive Learning , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[57]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  Yang Liu,et al.  Combining integrated sampling with SVM ensembles for learning from imbalanced datasets , 2011, Inf. Process. Manag..

[59]  Sofia Visa,et al.  Fuzzy Classifiers for Imbalanced , Complex Classes of Varying Size , 2005 .

[60]  Rushi Longadge,et al.  Class Imbalance Problem in Data Mining Review , 2013, ArXiv.

[61]  Antonio J. Rivera,et al.  Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets , 2014, Appl. Soft Comput..

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

[63]  Xin Yao,et al.  Multiclass Imbalance Problems: Analysis and Potential Solutions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[64]  Safdar Ali,et al.  Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines , 2014, Comput. Methods Programs Biomed..

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

[66]  Rong Yan,et al.  On predicting rare classes with SVM ensembles in scene classification , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[67]  Vasile Palade,et al.  A New Performance Measure for Class Imbalance Learning. Application to Bioinformatics Problems , 2009, 2009 International Conference on Machine Learning and Applications.

[68]  Sheng Chen,et al.  A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems , 2011, Neurocomputing.

[69]  Kai Ming Ting,et al.  A Comparative Study of Cost-Sensitive Boosting Algorithms , 2000, ICML.

[70]  Yuan-chin Ivan Chang,et al.  A modified area under the ROC curve and its application to marker selection and classification , 2014 .

[71]  Zhaohui Wu,et al.  Advanced Data Mining and Applications , 2013, Lecture Notes in Computer Science.