Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data

Lango, Mateusz; Stefanowski, Jerzy

doi:10.1007/s10844-017-0446-7

Cited by 67 publications

(38 citation statements)

References 51 publications

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“…On the other hand, the one‐vs‐one technique trains classifiers that are quadratic with respect to the number of class types, which can be costly when the number of classes is high. To handle class imbalance, various techniques can be used such as undersampling, oversampling, assigning weights to data samples belonging to different classes, and techniques such as multi‐class roughly balanced bagging 76 …”

Section: Resultsmentioning

confidence: 99%

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Görmez

Aydın

Karademir³

et al. 2020

Int J Communication

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Summary Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.

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Section: Resultsmentioning

confidence: 99%

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Görmez

Aydın

Karademir³

et al. 2020

Int J Communication

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“…The related standard approaches are Global-CS and Static-SMOTE as representatives of over sampling applied to single classifiers, decomposition with OVA and OVO ensembles with resampling of the binary classes done with random over sampling (ROS) or random under sampling (RUS) following recommendations of (Fernandez et al, 2013) and (Galar et al, 2011) and NCR as a more informative under sampling (Laurikkala, 2001), and newly introduced Multi-class Roughly Balanced Bagging, which showed good experimental results in the work of Lango and Stefanowski (2018).…”

Section: Methodsmentioning

confidence: 99%

“…Recently, yet another extension of Roughly Balanced Bagging to multiclass imbalanced data has been proposed by Lango and Stefanowski (2018).…”

Section: Related Work On Multiclass Imbalancesmentioning

confidence: 99%

Using Information on Class Interrelations to Improve Classification of Multiclass Imbalanced Data: A New Resampling Algorithm

Janicka

Lango

Stefanowski

2019

International Journal of Applied Mathematics and Computer Science

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The relations between multiple imbalanced classes can be handled with a specialized approach which evaluates types of examples’ difficulty based on an analysis of the class distribution in the examples’ neighborhood, additionally exploiting information about the similarity of neighboring classes. In this paper, we demonstrate that such an approach can be implemented as a data preprocessing technique and that it can improve the performance of various classifiers on multiclass imbalanced datasets. It has led us to the introduction of a new resampling algorithm, called Similarity Oversampling and Undersampling Preprocessing (SOUP), which resamples examples according to their difficulty. Its experimental evaluation on real and artificial datasets has shown that it is competitive with the most popular decomposition ensembles and better than specialized preprocessing techniques for multi-imbalanced problems.

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“…Most-popular data-level approaches include extensions of the SMOTE algorithm into a multi-class setting [22,23,24], strategies using feature selection [25,26], and alternative methods for instance generation by using Mahalanobis distance [27,28]. Algorithm-level solutions include decision tree adaptations [29], cost-sensitive matrix learning [30], and ensemble solutions utilizing Bagging [31,32] and Boosting [5,33].…”

Section: Multi-class Imbalanced Problemsmentioning

confidence: 99%

CCR: A combined cleaning and resampling algorithm for imbalanced data classification

Koziarski

Woźniak

2017

International Journal of Applied Mathematics and Computer Science

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Imbalanced data classification is one of the most widespread challenges in contemporary pattern recognition. Varying levels of imbalance may be observed in most real datasets, affecting the performance of classification algorithms. Particularly, high levels of imbalance make serious difficulties, often requiring the use of specially designed methods. In such cases the most important issue is often to properly detect minority examples, but at the same time the performance on the majority class cannot be neglected. In this paper we describe a novel resampling technique focused on proper detection of minority examples in a two-class imbalanced data task. The proposed method combines cleaning the decision border around minority objects with guided synthetic oversampling. Results of the conducted experimental study indicate that the proposed algorithm usually outperforms the conventional oversampling approaches, especially when the detection of minority examples is considered.

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Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data

Cited by 67 publications

References 51 publications

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Using Information on Class Interrelations to Improve Classification of Multiclass Imbalanced Data: A New Resampling Algorithm

CCR: A combined cleaning and resampling algorithm for imbalanced data classification

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