Class imbalances versus small disjuncts
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
IEEE Transactions on Knowledge and Data Engineering
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
An empirical study of the behavior of classifiers on imbalanced and overlapped data sets
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
Cost-sensitive decision tree ensembles for effective imbalanced classification
Applied Soft Computing
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The characteristics of the minority class distribution in imbalanced data is studied. Four types of minority examples --- safe, borderline, rare and outlier --- are distinguished and analysed. We propose a new method for identification of these examples in the data, based on analysing the local neighbourhoods of examples. Its application to UCI imbalanced datasets shows that the minority class is often scattered without too many safe examples. This characteristics of data distributions is also confirmed by another analysis with Multidimensional Scaling visualization. We examine the influence of these types of examples on 6 different classifiers learned over various real-world datasets. Results of experiments show that the particular classifiers reveal different sensitivity to the type of examples.