An evaluation of feature selection methods for environmental data

We present a comprehensive experimental study of 12 individual as well as 6 ensemble methods for feature selection for classification tasks on environmental data, more specifically on the species distribution modeling domain. The individual methods span all 3 categories, i.e. filter, wrapper, and embedded feature selection. Experiments on 8 environmental datasets show that Shapley Additive Explanations (SHAP) and Permutation Importance are the most effective individual methods, both from the wrapper category. Generally, filter methods perform poorly, and embedded methods fall in-between. Of the 2 machine learning algorithms used, Random Forest and LightGBM, the latter prevailed. Of the 6 ensemble methods considered, i.e. Borda Count, Condorcet, Coombs, Bucklin, Instant Runoff, and Reciprocal Ranking, the last one performs best, outperforming every other method, individual or ensemble, and has a high stability.