Geometric Structural Ensemble Learning for Imbalanced Problems

Zhu, Zonghai; Wang, Zhe; Li, Dongdong; Zhu, Yong; Du, Wenli

doi:10.1109/tcyb.2018.2877663

Cited by 102 publications

(30 citation statements)

References 36 publications

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“…Wong et al [30] proposed a cost-sensitive stacked denoising autoencoder ensemble method, and applied to address class imbalance problems in business domain. Zhu et al [31] proposed a geometric structural ensemble learning framework, which partitions and eliminates redundant majority samples by generating hyper-sphere through the Euclidean metric and learns basic classifiers to enclose the minority samples. Yang et al [32] proposed a hybrid ensemble classifier framework that combines density-based undersampling and cost-effective methods using multi-objective optimization algorithm to handle two issues: (1) undersampling methods suffer from losing important information; (2) cost-sensitive methods are sensitive to outliers and noise.…”

Section: Related Workmentioning

confidence: 99%

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Zhai

Zhang

2020

IEEE Access

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Binary imbalance problem refers to such a classification scenario where one class contains a large number of samples while another class contains only a few samples. When traditional classifiers face with imbalanced datasets, they usually bias towards majority class resulting in poor classification performance. Oversampling is an effective method to address this problem, yet how to conduct diversity oversampling is a challenge. In this paper, we proposed a diversity oversampling method based on a modified D2GAN model, and on the basis of diversity oversampling, we also proposed a binary imbalanced data classification approach based on classifier fusion by fuzzy integral. Extensive experiments are conducted on 8 data sets to compare the proposed methods with 7 state-of-the-art methods on 5 aspects: MMD-score, Silhouette-score, F-measure, G-means, and AUC-area. The 7 methods include 4 SMOTE related approaches and 3 GAN related approaches. The experimental results demonstrate that the proposed methods are more effective and efficient than the compared approaches. INDEX TERMS Binary class imbalance, diversity oversampling, generative adversarial network, classifier fusion, fuzzy integral

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Section: Related Workmentioning

confidence: 99%

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Zhai

Zhang

2020

IEEE Access

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“…In summary, in recent years, in addition to traditional machine learning and deep learning methods, the classification method based on ensemble learning [25,26] has gradually attracted the attention of many researchers and achieved good results in the emotion recognition of EEG signals. However, some problems still exist in this kind of method, such as features extracted from EEG signals which are not so typical and can reflect emotional information well, and the performance of models need to be improved.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

Chen

Chang

Guo

2021

Mathematical Problems in Engineering

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In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved.

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“…The third is hybrid approach, which combines the sampling methods with the algorithm‐level approaches to form an ensemble system or a neural network 4 . Ensemble methods 14–16 improve the classification performance by training several different classifiers and combining their predictions to output a single‐class label. However, since the overall accuracy is not appropriate for the imbalanced data sets, 17 the ensemble method is not enough to deal with the imbalanced situations.…”

Section: Introductionmentioning

confidence: 99%

Entropy‐based hybrid sampling ensemble learning for imbalanced data

Chi

Wang

et al. 2021

Int J Intell Syst

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Sampling method is one of the most commonly used techniques in dealing with imbalanced data. Most of the existing undersampling methods randomly select samples from negative class with replacement. However, it may lose some important information of the training data. Moreover, increasing the positive data by oversampling in high imbalanced situations may cause the overlapping problem. To overcome these problems, this paper proposes a hybrid sampling method. The method takes the distributions of the training data into consideration by the information entropy, thus distinguishing the important samples in the undersampling procedure. Meanwhile, since the positive data only extend to the size of each subset of the negative class in the oversampling, the overlapping problem is relieved.Further, the method retains all the data in the training procedure and generates various data views from the original training data. Then each view is handled with an individual basic classifier. Finally, all the basic classifiers are combined by the ensemble method. The newly proposed method is named as Entropy-based Hybrid Sampling Ensemble Learning (EHSEL). In addition, the EHSEL is applied to three different kinds of basic classifiers to validate its robustness. Experiments results show the great effectiveness of the EHSEL on real-world imbalanced data sets.

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Geometric Structural Ensemble Learning for Imbalanced Problems

Cited by 102 publications

References 36 publications

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

Entropy‐based hybrid sampling ensemble learning for imbalanced data

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