Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

Sun, Bo; Chen, Haiyan; Wang, Jiandong; Xie, Hua

doi:10.1007/s11704-016-5306-z

Cited by 67 publications

(31 citation statements)

References 43 publications

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“…Ensemble learning is one of the most popular methods at present. It has near-optimal classification methods for any problem, and it can achieve better generalization performance than a single classifier by training multiple individual classifiers and combining them together [8,[20][21][22][23][24][25][26][27][28][29][30][31][32]. There are two main approaches of ensemble learning: Bagging and Boosting.…”

Section: Related Workmentioning

confidence: 99%

“…Minaei-Bidgoli [23] proposed an ensemble-based approach for feature selection in order to overcome the problem of parameter sensitivity of feature selection approaches. In many cases, it is better to combine resampling with ensemble learning [26][27][28][29][30][31][32]. Kang et al [26] proposed EUS (ensemble undersampling), which selects the same number of samples from the majority class as the minority class to form several balanced subsets, and trains SVM-based classifiers for each subset to overcome the problem of information loss in undersampling to a certain extent.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

et al. 2020

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Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the number of one sample is larger than that of another, but the classification effect is not ideal. Some data preprocessing methods have been proposed to reduce the imbalance ratio of data sets and combine with the basic classifiers to get better performance. In order to improve the whole classification accuracy, we propose a novel classifier ensemble framework based on K-means and resampling technique (EKR). First, we divide the data samples in the majority class into several sub-clusters using K-means, k-value is determined by Average Silhouette Coefficient, and then adjust the number of data samples of each sub-cluster to be the same as that of the minority classes through resampling technology, after that each adjusted sub-cluster and the minority class are combined into several balanced subsets, the base classifier is trained on each balanced subset separately, and finally integrated into a strong ensemble classifier. In this paper, the extensive experimental results on 16 imbalanced datasets demonstrate the effectiveness and feasibility of the proposed algorithm in terms of multiple evaluation criteria, and EKR can achieve better performance when compared with several classical imbalanced classification algorithms using different data preprocessing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

et al. 2020

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“…Leo Breiman proposed Bagging algorithm in 1996 [20] and the deviation variance theory is used to make a complete and reasonable theoretical explanation of its effectiveness [26], [27]. The basic idea of Bagging algorithm is below: Given a weak classifier and a training set, the weak classification algorithm is used to classify the training samples.…”

Section: Classification Prediction By Bp Neural Network Ensemble Mmentioning

confidence: 99%

Application of BP Neural Network Ensemble Model Based on Bagging Algorithm

Liao¹,

Liao²,

Lv³

et al. 2019

IJMLC

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The latest coal mine policy of China puts forward new requirements for safe mining in the coalmine. This paper proposes a BP neural network ensemble learning model based on additional momentum term combined with Bagging algorithm for the prediction of coal and gas outburst. The additional momentum term approach is used to optimize the weights and thresholds of BP neural network during the training process. In order to enhance the prediction ability of BP neural network, the Bagging algorithm is adopted to build ensemble neural network. Experiments show that as compared with the standard BP neural network, the ensemble learning model can improve the prediction accuracy of 8%-10% points in the prediction of coal and gas outburst and the prediction accuracy is over 95%.Index Terms-BP neural network, Bagging algorithm, ensemble learning model, additional momentum terms, coal and gas outburst prediction.

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“…Additionally, the researchers also note that k-nearest neighbor based bagging algorithm gave the best results and hence commented that in cases involving big unbalanced credit coring datasets [56]. Other applications of bagging include network intrusion detection to enhance true positives and reduce false negatives [50], credit card fraud detection [51], medical diagnosis of arrhythmia beats [52], Urban traffic flow forecasting [53], forecasting of wind and solar power [54] and imbalanced data classification using Evolutionary under-sampling bootstrap aggregation models [55].…”

Section: How Bagging Workmentioning

confidence: 99%

The Superiority of the Ensemble Classification Methods: A Comprehensive Review

Nzuva¹,

Nderu²

2019

JIEA

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The modern technologies, which are characterized by cyber-physical systems and internet of things expose organizations to big data, which in turn can be processed to derive actionable knowledge. Machine learning techniques have vastly been employed in both supervised and unsupervised environments in an effort to develop systems that are capable of making feasible decisions in light of past data. In order to enhance the accuracy of supervised learning algorithms, various classification-based ensemble methods have been developed. Herein, we review the superiority exhibited by ensemble learning algorithms based on the past that has been carried out over the years. Moreover, we proceed to compare and discuss the common classification-based ensemble methods, with an emphasis on the boosting and bagging ensemble-learning models. We conclude by out setting the superiority of the ensemble learning models over individual base learners.

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Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

Cited by 67 publications

References 43 publications

A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data

Application of BP Neural Network Ensemble Model Based on Bagging Algorithm

The Superiority of the Ensemble Classification Methods: A Comprehensive Review

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