Surface Water Quality Classification via CMAES Ensemble Method

Abstract

In order to improve the quality of people’s daily life, the government departments continue to strengthen water quality management. However, artificial classification method cannot meet the needs of real-time processing, additionally the classification accuracy of traditional machine learning methods is not high enough. Ensemble learning uses multiple learning algorithms to obtain better prediction performance than a single learning algorithm. First of all, this paper briefly introduces ensemble learning, the Bagging and Boosting algorithms, and then proposes an ensemble learning method based on the covariance matrix adaptation evolution strategy (CMAES) algorithm. Next, data processing method, model evaluation method and index are introduced. Finally, the CMAES ensemble method is used to ensemble the following ten models, including logistic regression, linear discriminant analysis, support vector machine, decision tree, completely-random tree, naive Bayes, K-nearest neighbors, random forest, completely-random tree forest and deep cascade forest. Experiments show that the CMAES ensemble method is superior to all the other models, and this method will continue to be applied in future research.

Keywords

• water quality classification
• boosting
• covariance matrix adaptation evolution strategy (cmaes)
• ens-emble learning
• parameter optimization