Robust regularized extreme learning machine with asymmetric Huber loss function

Gupta, Deepak; Hazarika, Barenya Bikash; Berlin, M.

doi:10.1007/s00521-020-04741-w

Cited by 57 publications

(16 citation statements)

References 54 publications

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“…Wang et al [6] used a patient information based algorithm (PIBA) for real-time COVID-19 estimation. Very recently, Javid et al [7] used the ELM model [8] using a sliding window method for COVID-19 time-series forecasting to avoid the overfitting of data. A sliding window approach is also used to handle the non-stationarity of data.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Hazarika

Gupta

2020

Applied Soft Computing

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105

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Researchers around the world are applying various prediction models for COVID-19 to make informed decisions and impose appropriate control measures. Because of a high degree of uncertainty and lack of necessary data, the traditional models showed low accuracy over the long term forecast. Although the literature contains several attempts to address this issue, there is a need to improve the essential prediction capability of existing models. Therefore, this study focuses on modelling and forecasting of COVID-19 spread in the top 5 worst-hit countries as per the reports on 10th July 2020. They are Brazil, India, Peru, Russia and the USA. For this purpose, the popular and powerful random vector functional link (RVFL) network is hybridized with 1-D discrete wavelet transform and a wavelet-coupled RVFL (WCRVFL) network is proposed. The prediction performance of the proposed model is compared with the state-of-the-art support vector regression (SVR) model and the conventional RVFL model. A 60 day ahead daily forecasting is also shown for the proposed model. Experimental results indicate the potential of the WCRVFL model for COVID-19 spread forecasting.

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Section: Introductionmentioning

confidence: 99%

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Hazarika

Gupta

2020

Applied Soft Computing

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105

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“…This can be used to approximate ρ 0.5 . Recently, Gupta et al (2020) introduced an asymmetric version of the Huber loss:…”

Section: U-net Model For Pollutant Particulate Mattermentioning

confidence: 99%

In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

Shelton¹,

Polewski²,

Yao³

2021

Preprint

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Since the outbreak of COVID-19 policy makers have been relying upon non-pharmacological interventions to control the outbreak. With air pollution as a potential transmission vector there is need to include it in intervention strategies. We propose a U-net driven quantile regression model to predict P M 2.5 air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct P M 2.5 concentrations on ground-truth data and predict reasonable P M 2.5 values with their spatial distribution, even for locations where pollution data is unavailable. Such predictions of P M 2.5 characteristics could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.

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“…For example, Zhang et al [46], Chang et al [6], Zhang and Suganthan [47] and more. However, recently the growing popularity of extreme learning machine (ELM) [21,27] is because of its high generalization performance with low computational cost [2,14,15,18,20,38]. Peng et al [34] proposed a novel discriminative graph regularized extreme learning machine (GELM) model to improve the classification ability of ELM.…”

Section: Introductionmentioning

confidence: 99%

1-Norm random vector functional link networks for classification problems

Hazarika

Gupta

2022

Complex Intell. Syst.

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This paper presents a novel random vector functional link (RVFL) formulation called the 1-norm RVFL (1N RVFL) networks, for solving the binary classification problems. The solution to the optimization problem of 1N RVFL is obtained by solving its exterior dual penalty problem using a Newton technique. The 1-norm makes the model robust and delivers sparse outputs, which is the fundamental advantage of this model. The sparse output indicates that most of the elements in the output matrix are zero; hence, the decision function can be achieved by incorporating lesser hidden nodes compared to the conventional RVFL model. 1N RVFL produces a classifier that is based on a smaller number of input features. To put it another way, this method will suppress the neurons in the hidden layer. Statistical analyses have been carried out on several real-world benchmark datasets. The proposed 1N RVFL with two activation functions viz., ReLU and sine are used in this work. The classification accuracies of 1N RVFL are compared with the extreme learning machine (ELM), kernel ridge regression (KRR), RVFL, kernel RVFL (K-RVFL) and generalized Lagrangian twin RVFL (GLTRVFL) networks. The experimental results with comparable or better accuracy indicate the effectiveness and usability of 1N RVFL for solving binary classification problems.

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Robust regularized extreme learning machine with asymmetric Huber loss function

Cited by 57 publications

References 54 publications

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

Modelling and forecasting of COVID-19 spread using wavelet-coupled random vector functional link networks

In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

1-Norm random vector functional link networks for classification problems

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