Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm
Pattern Recognition Letters
Improving Identification of Difficult Small Classes by Balancing Class Distribution
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Editorial: special issue on learning from imbalanced data sets
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
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
A Study of Structural and Parametric Learning in XCS
Evolutionary Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
The effect of imbalanced data sets on LDA: A theoretical and empirical analysis
Pattern Recognition
International Journal of Approximate Reasoning
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Natural language tagging with genetic algorithms
Information Processing Letters
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
Feature-based image registration by means of the CHC evolutionary algorithm
Image and Vision Computing
A proposal of evolutionary prototype selection for class imbalance problems
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Imbalanced learning with a biased minimax probability machine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Evolutionary selection of hyperrectangles in nested generalized exemplar learning
Applied Soft Computing
Class imbalance methods for translation initiation site recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Exploring the performance of resampling strategies for the class imbalance problem
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
A preliminary study on the selection of generalized instances for imbalanced classification
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Classification of high dimensional and imbalanced hyperspectral imagery data
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Instance selection for class imbalanced problems by means of selecting instances more than once
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Evolutionary algorithms for the design of grid-connected PV-systems
Expert Systems with Applications: An International Journal
Editorial: Large scale instance selection by means of federal instance selection
Data & Knowledge Engineering
Pattern Recognition Letters
A scalable approach to simultaneous evolutionary instance and feature selection
Information Sciences: an International Journal
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Learning with imbalanced data is one of the recent challenges in machine learning. Various solutions have been proposed in order to find a treatment for this problem, such as modifying methods or the application of a preprocessing stage. Within the preprocessing focused on balancing data, two tendencies exist: reduce the set of examples (undersampling) or replicate minority class examples (oversampling). Undersampling with imbalanced datasets could be considered as a prototype selection procedure with the purpose of balancing datasets to achieve a high classification rate, avoiding the bias toward majority class examples. Evolutionary algorithms have been used for classical prototype selection showing good results, where the fitness function is associated to the classification and reduction rates. In this paper, we propose a set of methods called evolutionary undersampling that take into consideration the nature of the problem and use different fitness functions for getting a good trade-off between balance of distribution of classes and performance. The study includes a taxonomy of the approaches and an overall comparison among our models and state of the art undersampling methods. The results have been contrasted by using nonparametric statistical procedures and show that evolutionary undersampling outperforms the nonevolutionary models when the degree of imbalance is increased.