Notice of Removal: Optimal Weighted Extreme Learning Machine for Imbalanced Learning with Differential Evolution [Research Frontier]

Ri, JongHyok; Liu, Liang; Liu, Yong; Wu, Huifeng; Huang, Wen; Kim, Hun

doi:10.1109/mci.2018.2840707

Cited by 8 publications

(2 citation statements)

References 41 publications

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“…Regularization is to prevent overfitting, while kernel function is constructed on the basis of regularization [17]- [24]. To achieve parameter optimization, a combination of methods is adopted, including genetic algorithm, particle swarm optimization, firefly algorithm [25]- [30].…”

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm

et al. 2020

IEEE Access

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As fossil fuel is being depleted, the percentage of wind power capacity in total electricity generation is increasing. In order to improve the absorption capacity of wind power, wind power prediction has been introduced. Aiming at the disadvantage of low prediction accuracy and unstable model of traditional extreme learning machine (ELM), a kernel extreme learning machine based on differential evolution (DE) and cross validation optimization method is proposed to predict short-term wind power generation. Firstly, the average mean square error (MSE) verified by k folding and cross validation is adopted as the error function of the model to improve the stability and generalization performance of the model. Secondly, differential evolution algorithm is used to optimize the regularization coefficient and kernel width of the kernel extreme learning machine with cross validation and improve the precision of model is 8.34%. Finally, compared with the application of extreme learning machine with genetic algorithm and cross validation to a wind farm prediction case in northwest China, the experimental results show that the convergence rate of this method is twice that of genetic algorithm (GA) optimization algorithm, and the accuracy is higher.INDEX TERMS Differential evolutionary algorithm, Kernel extreme learning machine, k fold cross validation, wind power prediction.

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Section: Introductionmentioning

confidence: 99%

Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm

et al. 2020

IEEE Access

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“…Both approaches can be easily criticized, the first one can remove important information from the training set, while the second can lead to overfitting by duplicating examples. Methods which adapt learning algorithms rely on increasing attention to examples from minority classes, by using selected weights [11]- [14]. The basic difficulty is the appropriate selection of the weights.…”

mentioning

confidence: 99%

Fuzzy Ordered $c$-Means Clustering and Least Angle Regression for Fuzzy Rule-Based Classifier: Study for Imbalanced Data

Łęski

Czabański

Jeżewski

et al. 2020

IEEE Trans. Fuzzy Syst.

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This article introduces a new classifier design method that is based on a modification of the traditional fuzzy clustering. First, a new fuzzy ordered c-means clustering is proposed. This method can be considered as a generalization of the concept of the conditional fuzzy clustering by introducing ordering and weighting distances from data to cluster prototypes. As a result, a more local impact of data on created groups and increased repulsive force between group prototypes are obtained. The proposed method provides a better representation of the data classes, in particular for classes with small cardinality in the training set (imbalanced data). A special initialization of the prototypes is also introduced. Next, the proposed clustering method is used to construct the premises of if-then rules of a fuzzy classifier. The conclusions of the rules are obtained by the least angle regression algorithm, which selects only those rules, that maximize the generalization ability of a classifier. Each if-then rule is represented in easily interpretable Mamdani-Assilian form. Finally, an extensive experimental analysis on 89 benchmark balanced and imbalanced datasets is performed to demonstrate the validity of the introduced classifier. Its competitiveness to state-of-the-art classifiers, with respect to both performance and interpretability, is shown as well.

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Probability cost function based weighted extreme learning machine

Hyok,

Hyok

2023

Multimed Tools Appl

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Notice of Removal: Optimal Weighted Extreme Learning Machine for Imbalanced Learning with Differential Evolution [Research Frontier]

Cited by 8 publications

References 41 publications

Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm

Wind Power Prediction of Kernel Extreme Learning Machine Based on Differential Evolution Algorithm and Cross Validation Algorithm

Fuzzy Ordered $c$-Means Clustering and Least Angle Regression for Fuzzy Rule-Based Classifier: Study for Imbalanced Data

Probability cost function based weighted extreme learning machine

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