Alternative Ensemble Classifier Based on Penalty Strategy for Improving Prediction Accuracy

Lopez, Cindy-Pamela; Tenemaza, Maritzol; Loza-Aguirre, Edison

doi:10.1007/978-3-030-02053-8_163

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In this work, we accessed the willingness to invest using several data-mining algorithms. The effectiveness of modeling accuracy was further reported in classification algorithm papers concerning the internet transfer reliability [36][37][38][39][40]. In another study, the valuation of business models of intelligent manufacturing with Internet of Things and machine learning was based on algorithmic performance and was tested by the criteria of minimal error, fitting accuracy, training time and internal memory usage [41].…”

Section: Discussionmentioning

confidence: 99%

Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

et al. 2020

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Willingness to invest in renewable energy sources (RES) is predictable under data mining classification methods. Data was collected from the area of Evia in Greece via a questionnaire survey by using a sample of 360 respondents. The questions focused on the respondents’ perceptions and offered benefits for wind energy, solar photovoltaics (PVs), small hydro parks and biomass investments. The classification algorithms of Bayesian Network classifier, Logistic Regression, Support Vector Machine (SVM), C4.5, k-Nearest Neighbors (k-NN) and Long Short Term Memory (LSTM) were used. The Bayesian Network classifier was the best method, with a prediction accuracy of 0.7942. The most important variables for the prediction of willingness to invest were the level of information, the level of acceptance and the contribution to sustainable development. Future studies should include data on state incentives and their impact on willingness to invest.

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Section: Discussionmentioning

confidence: 99%

Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

et al. 2020

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“…where k is the rate of growth, δ has the adjustment rate, m is the offset parameter, and γj is set to −sjδj to make the function continuous [15].…”

Section: Linear Trend and Changepointsmentioning

confidence: 99%

Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning

Roy¹,

Jose²,

Sunil³

et al. 2020

Preprint

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The sudden pervasive of severe acute respiratory syndrome Covid-19 has been leading the universe into a prominent crisis. It has influenced each zone, for example, industrial area, horticultural zone, Public transportation, economic zone, and so on. So as to see how Covid-19 affected the globe, we conducted an investigation characterizing the effects of the pandemic over the world using Machine Learning (ML) method. Prediction is a typical data science exercise that helps the administration with function planning, objective setting, and anomaly detection. We propose an additive regression model with interpretable parameters that can be naturally balanced by experts with domain intuition about the time series. We focus on global data beginning from 22nd January 2020, till 26th April 2020 and performed dynamic map visualization of Covid-19 expansion globally by date wise and predicting the spread of virus on all countries and continents. The major advantages of this work include accurate analysis of country-wise as well as province/state-wise confirmed cases, recovered cases, deaths, prediction of pandemic viral attack and how far it is expanding globally.

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Alternative Ensemble Classifier Based on Penalty Strategy for Improving Prediction Accuracy

Cited by 2 publications

References 7 publications

Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

Intelligent Predictive Analytics for Sustainable Business Investment in Renewable Energy Sources

Prediction and Spread Visualization of Covid-19 Pandemic Using Machine Learning

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