Listar por tema "Resampling"
Mostrando ítems 1-5 de 5
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Associative learning on imbalanced environments: An empirical study
Elsevier (2015-10)Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior ... -
Improving Risk Predictions by Preprocessing Imbalanced Credit Data
Springer Berlin Heidelberg (2012)Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring ... -
On the suitability of resampling techniques for the class imbalance problem in credit scoring
Palgrave Macmillan (2013)In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison with the class of non-defaulters is a very common situation, but it has still received little attention. ... -
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
Elsevier (2012)The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning ... -
Understanding the apparent superiority of over-sampling through an analysis of local information for class-imbalanced data
Elsevier (2020-11-15)Data plays a key role in the design of expert and intelligent systems and therefore, data preprocessing appears to be a critical step to produce high-quality data and build accurate machine learning models. Over the past ...