Instance-Based Learning Algorithms
Machine Learning
Editing for the k-nearest neighbors rule by a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Adapting classification rule induction to subgroup discovery
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
KDD'99 competition: knowledge discovery contest
ACM SIGKDD Explorations Newsletter
Subgroup Discovery with CN2-SD
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
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
Stratification for scaling up evolutionary prototype selection
Pattern Recognition Letters
Pattern Recognition Letters
Hybrid flexible neural-tree-based intrusion detection systems: Research Articles
International Journal of Intelligent Systems
A hierarchical SOM-based intrusion detection system
Engineering Applications of Artificial Intelligence
Introducing a very large dataset of handwritten Farsi digits and a study on their varieties
Pattern Recognition Letters
SD-map: a fast algorithm for exhaustive subgroup discovery
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Cluster-Based sampling approaches to imbalanced data distributions
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study
IEEE Transactions on Evolutionary Computation
An Automatically Tuning Intrusion Detection System
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
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The subgroup discovery is defined as: ''given a population of individuals and a property of those individuals, we are interested in finding a population of subgroups as large as possible and in having the most unusual statistical characteristic with respect to the property of interest''. The subgroup discovery algorithms have to face the scaling up problem which appears in the evaluation of large size data sets. In this paper we are interested in the extraction of subgroups from large size data sets. To avoid the scaling up problem, we propose the combination of stratification and instance selection algorithms for scaling down the data set before the subgroup discovery task. In addition, two new stratification models are proposed to increase the presence of minority classes in data sets, which affects to the subgroup discovery process on them. The results show that the subgroup discovery extraction can be executed on large data sets preprocessed independently of the presence of minority classes, which could not be executed in other way.