Applying a new localized generalization error model to design neural networks trained with extreme learning machine

Liu, Qiang; Yin, Jianping; Leung, Victor C. M.; Zhai, Junhai; Cai, Zhiping; Lin, Jiarun

doi:10.1007/s00521-014-1549-5

Cited by 43 publications

(8 citation statements)

References 13 publications

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“…This figure, makes it obvious that the proposed model outperforms the Tsang models in most of the classes including Normal, DoS, Probe and R2L with 89.31%, 99.27%, 84.16% and 48.13%, respectively. MOGFIDS provides the highest result in U2R class Figure 4 shows a comparison of the proposed system with those systems proposed in [24], [54] and [55] that have been tested on the KDDTest − 21 in terms of the classification accuracy. Among those systems, the proposed detection model achieved the best classification accuracy of 94.68%.…”

Section: Additional Comparisonmentioning

confidence: 99%

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi

Nanda

et al. 2016

IEEE Trans. Comput.

528

241

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Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

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Section: Additional Comparisonmentioning

confidence: 99%

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Ambusaidi

Nanda

et al. 2016

IEEE Trans. Comput.

528

241

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show abstract

“…Then, the SLFFNN training is changed to a least-square problem. The ELM algorithms implement regularization theory to define a target function as [38][39][40]…”

Section: Extreme Learning Machinementioning

confidence: 99%

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

et al. 2020

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Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

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“…Then, the SLFFNN training is changed to a least square problem. The ELM algorithms implement regularization theory to define a target function as following [38][39][40]:…”

Section: J=1…n (3)mentioning

confidence: 99%

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Nabipour¹,

Mosavi²,

Baghban³

et al. 2020

Preprint

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Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

show abstract

Applying a new localized generalization error model to design neural networks trained with extreme learning machine

Cited by 43 publications

References 13 publications

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

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