Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
The class imbalance problem: A systematic study
Intelligent Data Analysis
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
Maximizing classifier utility when there are data acquisition and modeling costs
Data Mining and Knowledge Discovery
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Evolutionary rule-based systems for imbalanced data sets
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
International Journal of Approximate Reasoning
Bayes Vector Quantizer for Class-Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Information Sciences: an International Journal
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Combating the Small Sample Class Imbalance Problem Using Feature Selection
IEEE Transactions on Knowledge and Data Engineering
A multi-objective optimisation approach for class imbalance learning
Pattern Recognition
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Class imbalance methods for translation initiation site recognition in DNA sequences
Knowledge-Based Systems
Hellinger distance decision trees are robust and skew-insensitive
Data Mining and Knowledge Discovery
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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The imbalanced class problem is related to the real-world application of classification in engineering. It is characterised by a very different distribution of examples among the classes. The condition of multiple imbalanced classes is more restrictive when the aim of the final system is to obtain the most accurate precision for each of the concepts of the problem. The goal of this work is to provide a thorough experimental analysis that will allow us to determine the behaviour of the different approaches proposed in the specialised literature. First, we will make use of binarization schemes, i.e., one versus one and one versus all, in order to apply the standard approaches to solving binary class imbalanced problems. Second, we will apply several ad hoc procedures which have been designed for the scenario of imbalanced data-sets with multiple classes. This experimental study will include several well-known algorithms from the literature such as decision trees, support vector machines and instance-based learning, with the intention of obtaining global conclusions from different classification paradigms. The extracted findings will be supported by a statistical comparative analysis using more than 20 data-sets from the KEEL repository.