Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Data preparation for data mining
Data preparation for data mining
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine 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
A memetic algorithm for evolutionary prototype selection: A scaling up approach
Pattern Recognition
Automatically countering imbalance and its empirical relationship to cost
Data Mining and Knowledge Discovery
Local distance-based classification
Knowledge-Based Systems
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
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Information Sciences: an International Journal
Information Sciences: an International Journal
Cost-sensitive classification with respect to waiting cost
Knowledge-Based Systems
Differential Evolution for learning the classification method PROAFTN
Knowledge-Based Systems
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
IEEE Transactions on Evolutionary Computation
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Computers in Biology and Medicine
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
Genetic algorithms in feature and instance selection
Knowledge-Based Systems
Fast instance selection for speeding up support vector machines
Knowledge-Based Systems
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
Predicting Protein-Ligand Binding Site Using Support Vector Machine with Protein Properties
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In supervised classification, we often encounter many real world problems in which the data do not have an equitable distribution among the different classes of the problem. In such cases, we are dealing with the so-called imbalanced data sets. One of the most used techniques to deal with this problem consists of preprocessing the data previously to the learning process. This paper proposes a method belonging to the family of the nested generalized exemplar that accomplishes learning by storing objects in Euclidean n-space. Classification of new data is performed by computing their distance to the nearest generalized exemplar. The method is optimized by the selection of the most suitable generalized exemplars based on evolutionary algorithms. An experimental analysis is carried out over a wide range of highly imbalanced data sets and uses the statistical tests suggested in the specialized literature. The results obtained show that our evolutionary proposal outperforms other classic and recent models in accuracy and requires to store a lower number of generalized examples.