Improving the Accuracy of Ensemble Classifier Prediction Model Based on FLAME Clustering with Random Forest Algorithm

Augusty, Seena Mary; Izudheen, Sminu

doi:10.1109/icacc.2013.58

Cited by 1 publication

(1 citation statement)

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“…This method significantly improves upon the logistic regression methodology that gave as an accuracy of 0.7037. Since the logistic regression [20] and the ordinary least squares regression [17,46] approaches are still widely used in studies for estimation of willingness to pay or willingness to invest, we proposed the use of the Bayesian Network classification for improving prediction accuracy [47][48][49][50], though this finding has to be further validated.…”

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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