Abstract
Support Vector Regression (SVR) is a new approach in machine learning for time series prediction showing good performance. A big challenge for achieving optimal accuracy is the choice of appropriate parameters. In this paper, a Novel Enhanced Differential Evolution (NEDE) algorithm is proposed to calculate the optimal SVR parameters, and the combination approach (NEDE-SVR) was applied to predict the incidences of Zoonotic Cutaneous Leishmaniasis (ZCL) diseases. The NEDE-SVR based prediction model incorporates the climate factors as predictor variables, determined by analyzing their time lags related to the ZCL incidence. Conducted experiments have shown that NEDE-SVR exhibits good competitive performance using past diseases and climate data to predict the future cases of the ZCL disease. Accurate and timely ZCL disease predictions could aid structure health responses by informing key preparation and mitigation efforts.
Highlights
Support Vector Regression (SVR) [7,8] [10,11] is commonly applied in predicting
We propose a novel enhanced differential evolution (NEDE) optimization algorithm combined with SVR
- Run a loop in the NEDE function until it meets stopping criteria; Step 4: Build an SVR model using the optimal parameter values generated at Step 3; Step 5: Evaluate the goodness of the NEDE-SVR model in predicting
Summary
Support Vector Regression (SVR) [7,8] [10,11] is commonly applied in predicting It presents good generalization performance and outperforms other methods in nonlinear predicting, including neural networks. The application of Differential Evolution algorithm in optimization problems has efficient performance and accuracy in terms of computing, as the proposed method was able to find the optimal parameter values of the SVR applied to the training dataset. We use such proposed approach to predict the ZCL disease, using a dataset of M’sila province (Algeria), expressed as monthly incidences from 2010 to 2020.
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