Instance-Based Learning Algorithms
Machine Learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
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
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
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
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
Neighbor-weighted K-nearest neighbor for unbalanced text corpus
Expert Systems with Applications: An International Journal
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Classification of weld flaws with imbalanced class data
Expert Systems with Applications: An International Journal
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Evolutionary data analysis for the class imbalance problem
Intelligent Data Analysis
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
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Unbalanced data in a classification problem appears when there are many more instances of some classes than others. Several solutions were proposed to solve this problem at data level by under-sampling. The aim of this work is to propose evolutionary prototype selection algorithms that tackle the problem of unbalanced data by using a new fitness function. The results obtained show that a balancing of data performed by evolutionary under-sampling outperforms previously proposed under-sampling methods in classification accuracy, obtaining reduced subsets and getting a good balance on data.