Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Self-Organizing Maps
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Feature selection for text categorization on imbalanced data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
End-point detection of the aerobic phase in a biological reactor using SOM and clustering algorithms
Engineering Applications of Artificial Intelligence
Fuzzy rule extraction using recombined RecBF for very-imbalanced datasets
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Improving SVM training by means of NTIL when the data sets are imbalanced
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Boosting prediction accuracy on imbalanced datasets with SVM ensembles
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Parallel perceptrons, activation margins and imbalanced training set pruning
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Fuzzy labeled self-organizing map with label-adjusted prototypes
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
The Complexity of the Batch Neural Gas Extended to Local PCA
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
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There are several successful approaches dealing with imbalanced datasets. In this paper, the Fuzzy Labeled Self-Organizing Map (FLSOM) is extended to work with that type of data. The proposed approach is based on assigning two different values in the learning rate depending on the data vector membership of the class. The technique is tested with several datasets and compared with other approaches. The results seem to prove that FLSOM with different rates is a suitable tool and allows understanding and visualizing the data such as overlapped clusters.