Listar por autoría "6c527aef-5fe4-4e25-add6-5fa50d622863"
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A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
Elsevier (2013)Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural ... -
Back propagation with balanced MSE cost Function and nearest neighbor editing for handling class overlap and class imbalance
Springer (2011)The class imbalance problem has been considered a critical factor for designing and constructing the supervised classifiers. In the case of artificial neural networks, this complexity negatively affects the generalization ...
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Edited Nearest Neighbor Rule for Improving Neural Networks Classifications
Springer Berlin Heidelberg (2010)The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples. Several approaches are focused to form training ... -
Resampling methods versus cost functions for training an MLP in the class imbalance context
Springer-Verlag (2011)The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process. Both methods try to compensate the ...
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Use of ensemble based on GA for imbalance problem
Springer Verlag (Germany) (2009)In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns ...
