A Nearest Hyperrectangle Learning Method
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Journal of Global Optimization
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Analysis of new techniques to obtain quality training sets
Pattern Recognition Letters - Special issue: Sibgrapi 2001
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on 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
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Rapid and brief communication: Center-based nearest neighbor classifier
Pattern Recognition
Improving nearest neighbor rule with a simple adaptive distance measure
Pattern Recognition Letters
Self-generating prototypes for pattern classification
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The class imbalance problem: A systematic study
Intelligent Data Analysis
On the k-NN performance in a challenging scenario of imbalance and overlapping
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
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
Particle swarm optimization for prototype reduction
Neurocomputing
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
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
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Evolutionary data analysis for the class imbalance problem
Intelligent Data Analysis
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Learning from imbalanced data in presence of noisy and borderline examples
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
IPADE: iterative prototype adjustment for nearest neighbor classification
IEEE Transactions on Neural Networks
Enhancing IPADE algorithm with a different individual codification
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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
A unifying view on dataset shift in classification
Pattern Recognition
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
Expert Systems with Applications: An International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Expert Systems with Applications: An International Journal
Hybridization of fuzzy GBML approaches for pattern classification problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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A wide number of real word applications presents a class distribution where examples belonging to one class heavily outnumber the examples in the other class. This is an arduous situation where standard classification techniques usually decrease their performance, creating a handicap to correctly identify the minority class, which is precisely the case under consideration in these applications. In this work, we propose the usage of the Iterative Instance Adjustment for Imbalanced Domains (IPADE-ID) algorithm. It is an evolutionary framework, which uses an instance generation technique, designed to face the existing imbalance modifying the original training set. The method, iteratively learns the appropriate number of examples that represent the classes and their particular positioning. The learning process contains three key operations in its design: a customized initialization procedure, an evolutionary optimization of the positioning of the examples and a selection of the most representative examples for each class. An experimental analysis is carried out with a wide range of highly imbalanced datasets over the proposal and recognized solutions to the problem. The results obtained, which have been contrasted through non-parametric statistical tests, show that our proposal outperforms previously proposed methods.