Journal Article10.1109/TSMCC.2011.2161285
A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches
Mikel Galar,Alberto Fernández,Edurne Barrenechea,Humberto Bustince,Francisco Herrera +4 more
- 01 Jul 2012
- Vol. 42, Iss: 4, pp 463-484
2.6K
TL;DR: 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 is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.
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Abstract: 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 one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. In machine learning, the ensemble of classifiers are known to increase the accuracy of single classifiers by combining several of them, but neither of these learning techniques alone solve the class imbalance problem, to deal with this issue the ensemble learning algorithms have to be designed specifically. In this paper, our aim is to review the state of the art on ensemble techniques in the framework of imbalanced data-sets, with focus on two-class problems. 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. In addition, we develop a thorough empirical comparison by the consideration of the most significant published approaches, within the families of the taxonomy proposed, to show whether any of them makes a difference. This comparison has shown the good behavior of the simplest approaches which combine random undersampling techniques with bagging or boosting ensembles. In addition, the positive synergy between sampling techniques and bagging has stood out. Furthermore, our results show empirically that ensemble-based algorithms are worthwhile since they outperform the mere use of preprocessing techniques before learning the classifier, therefore justifying the increase of complexity by means of a significant enhancement of the results.
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Citations
Error Awareness by Lower and Upper Bounds in Ensemble Learning
TL;DR: Experimental results would explore how LBER and UBEO would lead negative correlation learning towards a better decision boundary.
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Hypertension Type Classification Using Hierarchical Ensemble of One-Class Classifiers for Imbalanced Data
Bartosz Krawczyk,Michał Woźniak +1 more
- 09 Sep 2014
TL;DR: The research on the computer support system which is able to recognize the type of hypertension confirmed usefulness of the proposed, hierarchical one-class classifier ensemble and could be applied in the real medical decision support systems.
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Resampling strategies for imbalanced regression: a survey and empirical analysis
J. G. Avelino,George D. C. Cavalcanti,Rafael M. O. Cruz +2 more
TL;DR: This study surveys and empirically analyzes resampling strategies for imbalanced regression tasks, proposing a taxonomy and evaluating various balancing and predictive models using metrics to capture key elements in an imbalanced regression data context.
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Ensembles of cost-diverse Bayesian neural learners for imbalanced binary classification
TL;DR: This work proposes to create ensembles of the recently introduced binary Bayesian classifiers, that show intrinsic re-balancing capacities, by means of a diversification mechanism which is based on applying different cost policies to each ensemble learner as well as appropriate aggregation schemes.
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XPro: A Cross-End Processing Architecture for Data Analytics in Wearables
Aosen Wang,Lizhong Chen,Wenyao Xu +2 more
- 24 Jun 2017
TL;DR: The proposed cross-end architecture is able to realize a generic classification design across wearable sensors and a data aggregator with high energy-efficiency and, compared with state-of-the-art methods, can increase the battery life of the sensor node and reduce system delay.
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TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
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Leo Breiman
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TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.