LW-ELM: A Fast and Flexible Cost-Sensitive Learning Framework for Classifying Imbalanced Data

Yu, Hualong; Sun, Changyin; Yang, Xibei; Zheng, Shang; Wang, Qi; Xi, Xiaoyan

doi:10.1109/access.2018.2839340

Cited by 32 publications

(15 citation statements)

References 48 publications

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“…The second issue is a lack of mistake compensation flexibility. Reference [ 20 ] developed a marker-weighted extreme learning machine based on the concept of cost-sensitive learning to address the drawbacks of this technique. By increasing the expected output value of minority class labels, label-weighted extreme learning machines improve the training error tolerance of minority class cases.…”

Section: Imbalanced Extreme Learning Classification Algorithmmentioning

confidence: 99%

“…For the setting of label weights in two-class and multi-class classification problems, [ 20 ] provides two weight distribution methods, as follows:

where Δ(num i ) is the number of samples from the i th class in the training set and Δmajor(num) is the number of samples from the majority class. Two weight distribution methods are provided:

…”

Section: Imbalanced Extreme Learning Classification Algorithmmentioning

confidence: 99%

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A Novel Stress State Assessment Method for College Students Based on EEG

Liu

Gao

et al. 2022

Computational Intelligence and Neuroscience

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Stress is an unavoidable problem for today’s college students. Stress can arouse strong personal emotional and behavioral responses. Compared with other groups of the same age, college students have a special way of life and living environment. They have complex interpersonal relationships and relatively weak social support systems. At the same time, they also face fierce competition in both academic and employment. However, they lack the skills to deal with the crisis and are reluctant to ask others for help, which leads to a simultaneous increase in mental stress. The pressure on college students mainly comes from study, family, social, employment, society, and economy. When students face multiple pressures from family, school, society, etc., some students are prone to some psychological problems due to their own personality or external environment and other reasons. Therefore, regular assessment of students’ stress status is an important means to prevent college students’ psychological problems. Considering that in real life, the number of students whose pressure is within the tolerable range is the majority, while the number of students who are under too much pressure is a minority. Therefore, the actual dataset to be identified belongs to a kind of imbalanced data. In this study, an improved extreme learning machine (IELM) is used to improve the performance of the recognition model as much as possible. IELM takes the idea of label weighting as the starting point, introduces the AdaBoost algorithm, and combines its weight distribution with the label weighted extreme learning machine (ELM). During the weight update process, the advantage of the imbalanced nature of multi-label datasets is taken. IELM was used to classify EEG data to determine the stress level of college students. The experimental results demonstrate that the algorithm used in this study has excellent classification performance and can accurately assess students’ stress levels. The accurate assessment of stress has provided a solid foundation for the development of students’ mental health and has significant practical implications.

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Section: Imbalanced Extreme Learning Classification Algorithmmentioning

confidence: 99%

“…For the setting of label weights in two-class and multi-class classification problems, [ 20 ] provides two weight distribution methods, as follows:

where Δ(num i ) is the number of samples from the i th class in the training set and Δmajor(num) is the number of samples from the majority class. Two weight distribution methods are provided:

…”

Section: Imbalanced Extreme Learning Classification Algorithmmentioning

confidence: 99%

A Novel Stress State Assessment Method for College Students Based on EEG

Liu

Gao

et al. 2022

Computational Intelligence and Neuroscience

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“…In practical applications, two weighting schemes can be selected according to the sample distribution. Yu et al [20] proposed the label-weighted extreme learning machine (LW-ELM) algorithm to improve WELM. LW-ELM has a faster training speed on large-scale data sets by eliminating a large-matrix multiplication operation.…”

Section: Introductionmentioning

confidence: 99%

An Improved Extreme Learning Machine for Imbalanced Data Classification

Zhang

Qin

2022

IEEE Access

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In the field of machine learning, Extreme Learning Machine (ELM) has been widely used in classification and regression tasks. However, like many traditional machine learning algorithms, the classification results of ELM are often not good enough when facing imbalanced data. For this reason, we proposed an extreme learning machine algorithm with output weight adjustment called OWA-ELM, which can make the decision boundary of ELM move to majority classes, and improve the classification performance of imbalanced data. Specifically, in ELM, we add a reasonable increment Δ to the connection weights between hidden layer neurons and minority output neurons in ELM, so that the output value of minority output neurons increases. Finally, the classification accuracy of the minority class samples can be improved without significantly affecting the classification of the majority class samples. The performance of OWA-ELM was compared with ELM, WELM, CS-ELM and CCR-ELM. In the experiments on 22 data sets, OWA-ELM algorithm has achieved 9 times best and 4 times suboptimal results on G-mean. In F-measure, 13 best and 1 second best results were obtained. Therefore, the OWA-ELM algorithm is effective to deal with imbalanced data classification.

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“…Du et al [27] proposed an online sequential extreme learning machine with under-and oversampling(OSELM-UO) for online imbalanced big data classification. In addition, some ELM-based imbalanced methods, such as ensemble weighted ELM [28], classspecific cost regulation ELM [29], label-weighted extreme learning machine [30], and class-specific ELM [31], have also been proposed. However, to the best of our knowledge, there is no study that uses imbalanced ELM methods for epileptic EEG signal recognition; therefore, it is necessary to propose such a method for epileptic EEG signal recognition.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Imbalanced Epileptic EEG Signals by a Graph‐Based Extreme Learning Machine

Zhou

Zhang

Jiang

2021

Wireless Communications and Mobile Computing

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Epileptic EEG signal recognition is an important method for epilepsy detection. In essence, epileptic EEG signal recognition is a typical imbalanced classification task. However, traditional machine learning methods used for imbalanced epileptic EEG signal recognition face many challenges: (1) traditional machine learning methods often ignore the imbalance of epileptic EEG signals, which leads to misclassification of positive samples and may cause serious consequences and (2) the existing imbalanced classification methods ignore the interrelationship between samples, resulting in poor classification performance. To overcome these challenges, a graph-based extreme learning machine method (G-ELM) is proposed for imbalanced epileptic EEG signal recognition. The proposed method uses graph theory to construct a relationship graph of samples according to data distribution. Then, a model combining the relationship graph and ELM is constructed; it inherits the rapid learning and good generalization capabilities of ELM and improves the classification performance. Experiments on a real imbalanced epileptic EEG dataset demonstrated the effectiveness and applicability of the proposed method.

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LW-ELM: A Fast and Flexible Cost-Sensitive Learning Framework for Classifying Imbalanced Data

Cited by 32 publications

References 48 publications

A Novel Stress State Assessment Method for College Students Based on EEG

A Novel Stress State Assessment Method for College Students Based on EEG

An Improved Extreme Learning Machine for Imbalanced Data Classification

Recognition of Imbalanced Epileptic EEG Signals by a Graph‐Based Extreme Learning Machine

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