On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19

Benítez-Peña, Sandra; Carrizosa, Emilio; Guerrero, Vanesa; Jiménez-Gamero, M. D.; Martín-Barragán, Belén; Molero-Río, Cristina; Ramírez‐Cobo, Pepa; Morales, Dolores Romero; Sillero-Denamiel, M. Remedios

doi:10.1016/j.ejor.2021.04.016

Cited by 18 publications

(4 citation statements)

References 38 publications

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“…Lastly, 11 articles were identified within the Data Processing and Analysis component. Within this component, insight into archival data potentially harming prediction engines came to light (Benbya et al, 2021;Benítez-Peña et al, 2021), as well as the importance of accurate long-term forecasts (Feuerriegel and Gordon, 2019) and the consequences of imbalanced data sets (Gunnarsson et al, 2021;Lebovitz et al, 2021). Furthermore, it provided insight into some of the benefits of utilizing AI interpretability to expediently process and forecast data (Kraus et al, 2020;Ma and Fildes, 2021;Shin et al, 2020;Zhu et al, 2021) and ineffective regression models which are not reliably accurate (Pedro Duarte Silva, 2017).…”

Section: Tbd and Accuracymentioning

confidence: 99%

Transparent Artificial Intelligence and Human Resource Management: A Systematic Literature Review

Votto,

Liu

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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As the technological expansion of Artificial Intelligence (AI) penetrates various industries, HumanResource Management has attempted to keep pace with the new capabilities and challenges these technologies have brought. When adopting AI, transparency within HRM decisions is an increasing demand to establish ethical, unbiased, and fair practices within a firm. To this end, explainable AI (XAI) methods have become vital in achieving transparency within HRM decision-making. Thus, there has been a growing interest in exploring successful XAI techniques, as evidenced by the systematic literature review (SLR) performed in this paper. Our SLR starts by revealing where AI exists within HRM. Following this, we review the literature on XAI and accuracy, XAI design, accountability, and data processing initiatives within HRM. The integrated framework we propose provides an avenue to bridge the gap between transparent HRM practices and Artificial Intelligence, providing the industrial and academic community with better insight into where XAI could exist within HRM processes.

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Section: Tbd and Accuracymentioning

confidence: 99%

Transparent Artificial Intelligence and Human Resource Management: A Systematic Literature Review

Votto,

Liu

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…The approach in [12] was recently taken up in [4] in the context of predicting time series from the COVID-19 pandemic and an alternative penalizing term was introduced that is similar to the approach used in the LASSO regression model, where instead of models, variables are selected based on their marginal distribution.…”

Section: Related Workmentioning

confidence: 99%

Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications

Echtenbruck¹,

Echtenbruck²,

Batenburg³

et al. 2022

Preprint

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Automated model selection is often proposed to users to choose which machine learning model (or method) to apply to a given regression task. In this paper we show that combining different regression models can yield better results than selecting a single ('best') regression model, and outline an efficient method that obtains optimally weighted convex linear combination from a heterogeneous set of regression models. More specifically, in this paper a heuristic weight optimization, used in a preceding conference paper, is replaced by an exact optimization algorithm using convex quadratic programming. We prove convexity of the quadratic programming formulation for the straightforward formulation and for a formulation with weighted data points. The novel weight optimization is not only (more) exact but also more efficient. The methods we develop in this paper are implemented and made available via github-open source. They can be executed on commonly available hardware and offer a transparent and easy to interpret interface. The results indicate that the approach outperforms model selection methods on a range of data sets, including data sets with mixed variable type from drug discovery applications.

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“…To study vaccine efficacy trials, in [22] an ensemble learning algorithm is adopted. An optimisation algorithm is proposed in [23] to build a sparse ensemble algorithm to predict the evolution of COVID-19.…”

Section: A Previous Workmentioning

confidence: 99%

A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

Tayarani-Najaran

2022

IEEE Trans. Syst. Man Cybern, Syst.

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The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this paper, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behaviour. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multi-objective optimisation algorithm is developed which uses the model to search through the policy space and finds a set of policies that minimise the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modelling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this paper to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a meta-model algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The meta-model is a set of Support Vector Machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimisation process which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimisation algorithms are performed.

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On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19

Cited by 18 publications

References 38 publications

Transparent Artificial Intelligence and Human Resource Management: A Systematic Literature Review

Transparent Artificial Intelligence and Human Resource Management: A Systematic Literature Review

Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications

A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

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