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A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
TLDR
We propose a taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based. Expand
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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
TLDR
We analyse the performance of ensemble methods by binarization techniques for multi-class classification problems. Expand
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Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches
TLDR
The imbalanced class problem is related to the real-world application of classification in engineering. Expand
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EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
TLDR
We develop a new ensemble construction algorithm (EUSBoost) based on RUSBoost, one of the simplest and most accurate ensemble, which combines random undersampling with Boosting. Expand
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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
TLDR
This paper reviews the fingerprint classification literature looking at the problem from a double perspective. Expand
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Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness
TLDR
This paper aims to reach conclusions on such aspects focusing on the behavior, in terms of performance and robustness, of several Multiple Classifier Systems when these are trained with noisy data. Expand
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A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications
TLDR
We consider the problem of choosing a total order between intervals in multiexpert decision making problems. Expand
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Learning from Imbalanced Data Sets
TLDR
Data Science is a discipline for discovering new and significant relationships, patterns and trends in the examination of large amounts of data. Expand
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Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers
TLDR
We propose a dynamic classifier selection strategy for One-vs-One scheme that tries to avoid the non-competent classifiers when their output is probably not of interest. Expand
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