Machine Learning
Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary Computation
Expert Systems with Applications: An International Journal
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Open-source machine learning: R meets Weka
Computational Statistics - Proceedings of DSC 2007
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Predicting the potential habitat of oaks with data mining models and the R system
Environmental Modelling & Software
Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels
Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Modern Applied Statistics with S
Modern Applied Statistics with S
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Avoiding Pitfalls in Neural Network Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
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A hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) is proposed for predictive models in the environmental sciences. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Three environmental forecast datasets used in the WCCI-2006 contest - surface air temperature, precipitation and sulphur dioxide concentration - were tested. We used multiple linear regression (MLR) as benchmark and a variety of machine learning techniques including bootstrap-aggregated ensemble artificial neural network (ANN), SVR-ES, SVR with hyper-parameters given by the Cherkassky-Ma estimate, the M5 regression tree, and random forest (RF). We also tested all techniques using stepwise linear regression (SLR) first to screen out irrelevant predictors. We concluded that SVR-ES is an attractive approach because it tends to outperform the other techniques and can also be implemented in an almost automatic way. The Cherkassky-Ma estimate is a useful approach for minimizing the mean absolute error and saving computational time related to the hyper-parameter search. The ANN and RF are also good options to outperform multiple linear regression (MLR). Finally, the use of SLR for predictor selection can dramatically reduce computational time and often help to enhance accuracy.