An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace

Tian, Huixin; Mao, Zhizhong

doi:10.1109/tase.2008.2005640

Cited by 157 publications

(16 citation statements)

References 20 publications

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“…Lan et al [17] presented an enhanced integration algorithm with more stable performance and higher classification accuracy for Ensemble of Online Sequential ELM (EOS-ELM). Tian et al [18, 19] used the Bagging Integrated Model and the modified AdaBoost RT to modify the conventional ELM, respectively. Lu et al [20] proposed several algorithms to reduce the computational cost of the Moore-Penrose inverse matrices for ELM.…”

Section: Related Workmentioning

confidence: 99%

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification

Yan

et al. 2016

Computational Intelligence and Neuroscience

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Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

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Section: Related Workmentioning

confidence: 99%

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification

Yan

et al. 2016

Computational Intelligence and Neuroscience

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“…The PJM Interconnection supplies 50 million people in the United States, serves a peak load of 145,000MW with 165,000MW of generation, making it the world's largest electricity market [9][10][11][12]. While average hourly electricity prices in PJM's real-time market were between $49 and 58/MWh during 2006 -2008, peak prices went above $200/ MWh for 35 h in 2006, 2007 and 2008.…”

Section: Prediction Of Day-ahead Electricity Pricementioning

confidence: 99%

A novel prediction modeling scheme based on multiple information fusion for day-ahead electricity price

Tian¹,

Meng³

2011

2011 Chinese Control and Decision Conference (CCDC)

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On the basis of the information fusion idea, a novel multiple information fusion modeling method is proposed. Several artificial neural networks are used to fuse the information of data. And then the results of information fusion by ANNs will be fused again according to their performance. Using the novel multiple information fusion scheme, a new modeling approach is presented to establish the prediction model. The day-ahead electricity price prediction model is tested by the real data. The experiments demonstrate that the new prediction model established by multiple information fusion method has better performance.

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“…The most prominent ensemble machine generation methods are Bagging and Boosting (Freund & Schapir, 1996 [13].…”

Section: A Ensemble Scheme Selectedmentioning

confidence: 99%

Multiple ANNs combined scheme for fault diagnosis of power transformers

Tian

Meng

2011

2011 Chinese Control and Decision Conference (CCDC)

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It is very important to find the incipient faults of power transformers for keeping away from the further deterioration in modern power system. For enhancing the accuracy of fault diagnosis for power transformers, a multiple ANNs scheme is proposed in this paper. In this scheme, a novel ANN method ELM is used to establish the base classifier for its good performance and fast learning speed. Secondly, the several base classifiers based on single ELM will be combined by consulting ensemble techniques. And then a multiple ELMs method is obtained. The real gas records data from a power company is used to establish fault diagnosis system for power transformers based on the new multiple ELMs method. For comparison, the conventional method and other ANN method are used to build fault diagnosis models by the same data. The experiments demonstrate the new multiple ELMs method has the best performance in both learning ability aspect and generalization ability aspect for fault diagnosis of power transformers.

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An Ensemble ELM Based on Modified AdaBoost.RT Algorithm for Predicting the Temperature of Molten Steel in Ladle Furnace

Cited by 157 publications

References 20 publications

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification

A novel prediction modeling scheme based on multiple information fusion for day-ahead electricity price

Multiple ANNs combined scheme for fault diagnosis of power transformers

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