Ensemble Methods in Environmental Data Mining

Abstract

Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for envi ronmental data mining: (i) bagging , (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil. methods are majority voting, performance weighting, Bayesian combination, and vogging. Meta-learning methods learn from new training data created from the predictions of a set of base classifiers. The most well-known meta-learning methods are stacking strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection, (iii) boosting, and (iv) voting. In the experimental studies, ensemble methods are tested on different real-world environmental datasets.