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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
Classifier learning with data-sets that suffer from imbalanced class distributions is a challenging problem in data mining community. This issue occurs when the number of examples that represent oneExpand
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An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes
Classification problems involving multiple classes can be addressed in different ways. One of the most popular techniques consists in dividing the original data set into two-class subsets, learning aExpand
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Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
The imbalanced class problem is related to the real-world application of classification in engineering. It is characterised by a very different distribution of examples among the classes. TheExpand
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EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
Classification with imbalanced data-sets has become one of the most challenging problems in Data Mining. Being one class much more represented than the other produces undesirable effects in both theExpand
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A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation
A background and exhaustive survey on fingerprint matching methods in the literature is presented.A taxonomy of fingerprint minutiae-based methods is proposed.An extensive experimental study showsExpand
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A survey of fingerprint classification Part I: Taxonomies on feature extraction methods and learning models
This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models consideredExpand
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A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
We consider the problem of choosing a total order between intervals in multiexpert decision making problems. To do so, we first start researching the additivity of interval-valued aggregationExpand
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Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness
Traditional classifier learning algorithms build a unique classifier from the training data. Noisy data may deteriorate the performance of this classifier depending on the degree of sensitiveness toExpand
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Learning from Imbalanced Data Sets
Nowadays, the availability of large volumes of data and the widespread use of tools for the proper extraction of knowledge information has become very frequent, especially in large corporations. ThisExpand
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Evolutionary undersampling for extremely imbalanced big data classification under apache spark
The classification of datasets with a skewed class distribution is an important problem in data mining. Evolutionary undersampling of the majority class has proved to be a successful approach toExpand
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  • Open Access