Machine Learning
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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
The class imbalance problem: A systematic study
Intelligent Data Analysis
FLSOM with Different Rates for Classification in Imbalanced Datasets
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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This paper deals with the problem of training a discriminative classifier when the data sets are imbalanced. More specifically, this work is concerned with the problem of classify a sample as belonging, or not, to a Target Class (TC), when the number of examples from the “Non-Target Class” (NTC) is much higher than those of the TC. The effectiveness of the heuristic method called Non Target Incremental Learning (NTIL) in the task of extracting, from the pool of NTC representatives, the most discriminant training subset with regard to the TC, has been proved when an Artificial Neural Network is used as classifier (ISMIS 2003). In this paper the effectiveness of this method is also shown for Support Vector Machines.