EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

Galar, Mikel; Fernández, Alberto; Barrenechea, Edurne; Herrera, Francisco

doi:10.1016/j.patcog.2013.05.006

Cited by 364 publications

(173 citation statements)

References 49 publications

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“…It biases SVM in a way that will push the boundary away from the positive instances using different error costs for the positive and negative classes. 7) EUSBOOST [21] : An ensemble method that uses Evolutionary UnderSampling (EUS, [25]) guided boosting. EUS arises from the application of evolutionary prototype selection algorithms to imbalanced domains.…”

Section: A Two-class Imbalanced Classification: Models and Evaluationmentioning

confidence: 99%

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Ramentol

Vluymans

Verbiest

et al. 2015

IEEE Trans. Fuzzy Syst.

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Abstract-Imbalanced classification deals with learning from data with a disproportional number of samples in its classes. Traditional classifiers exhibit poor behavior when facing this kind of data because they do not take into account the imbalanced class distribution. Four main kinds of solutions exist to solve this problem: modifying the data distribution, modifying the learning algorithm for considering the imbalance representation, including the use of costs for data samples, and ensemble methods. In this paper, we adopt the second type of solution and introduce a classification algorithm for imbalanced data that uses fuzzy rough set theory and ordered weighted average aggregation. The proposal considers different strategies to build a weight vector to take into account data imbalance. Our methods are validated by an extensive experimental study, showing statistically better results than 13 other state-of-the-art methods.Index Terms-Fuzzy rough sets, imbalanced classification, machine learning, ordered weighted average (OWA).

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Section: A Two-class Imbalanced Classification: Models and Evaluationmentioning

confidence: 99%

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

Ramentol

Vluymans

Verbiest

et al. 2015

IEEE Trans. Fuzzy Syst.

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“…Other ensemble-based approaches analyze the influence of noisy data in imbalanced classification [43]. Prominent and recent examples include the SMOTEBoost [44], SMOTEBagging [45], RB-Bagging [46], NBBag [47], and EUSBoost [48] methods. Very recent proposals also deal with multi-class imbalanced data [49].…”

Section: Imbalanced Classificationmentioning

confidence: 99%

“…This procedure is common practice and has been used in other experimental studies as well (e.g. [2,48,52,61]). …”

Section: Datasetsmentioning

confidence: 99%

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

et al. 2016

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Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of realworld situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals.

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“…The experimental set-up has been designed following the common practice (Akbani et al, 2004;Barua et al, 2014;Díez-Pastor et al, 2015;Galar et al, 2013;López et al, 2012López et al, , 2013Yu et al, 2013Yu et al, , 2015, which consists of generating skewed data sets with different levels of class imbalance. In particular, the scheme proposed in the literature is to transform multi-class data sets by combining several original classes to shape the majority and minority classes.…”

Section: Data Setsmentioning

confidence: 99%

Associative learning on imbalanced environments: An empirical study

Cleofas-Sánchez

Sánchez

García

et al. 2016

Expert Systems with Applications

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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 probabilities. However, in many real-life applications the ratios of prior probabilities between classes are extremely skewed. Although the literature has provided numerous studies that examine the performance degradation of renowned classifiers on different imbalanced scenarios, so far this effect has not been supported by a thorough empirical study in the context of associative memories. In this paper, we fix our attention on the applicability of the associative neural networks to the classification of imbalanced data. The key questions here addressed are whether these models perform better, the same or worse than other popular classifiers, how the level of imbalance affects their performance, and whether distinct resampling strategies produce a different impact on the associative memories. In order to answer these questions and gain further insight into the feasibility and efficiency of the associative memories, a large-scale experimental evaluation with 31 databases, seven classification models and four resampling algorithms is carried out here, along with a non-parametric statistical test to discover any significant differences between each pair of classifiers.

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EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

Cited by 364 publications

References 49 publications

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

Associative learning on imbalanced environments: An empirical study

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