Abstract
Machine learning has come a long way in recent decades due to huge increases in computing power and the availability of robust public platforms for statistical analysis (e.g., R Core Team 2016). Machine learning techniques have benefited from advances in statistical learning and vice versa (Hastie et al. 2009; Slavakis et al. 2014), resulting in impressive applications of big data in imaging, astronomy, medicine, finance and to a lesser extent in ecology (Van Horn and Toga 2014; Zhang and Zhao 2015; Belle et al. 2015; Hussain and Prieto 2016; Hampton et al. 2013). A healthy relationship with computer science and engineering has invigorated the field even more, resulting in a variety of techniques suitable for diverse applications. One successful and frequently used method is ensemble learning, where learning algorithms independently construct a set of classifiers or regression-estimates and classify or regress newer data points by either taking a weighted vote (classifiers) or an average (regression) of their predictions (Zhou 2012). A majority of the ensemble learning problems deal with classification due to the binary, or in some cases multinomial, response that is of interest. However, in the field of ecology, and especially in tree species abundance modeling, we have access to continuous data thanks to the Forest Inventory Analysis (FIA) in the United States (Woudenberg et al. 2010) that lends itself to a regression approach. Valuable information can be lost if the continuous data are classified a priori into classes. Therefore, it is best to solve the problem in a regression context, and classify the results later to retain most of the information in the response. I will choose the regression approach for this reason and also to highlight this less used aspect of statistical learning.
Citation
Prasad, Anantha M. 2018. Machine Learning for Macroscale Ecological Niche Modeling - a Multi-Model, Multi-Response Ensemble Technique for Tree Species Management Under Climate Change. In: Humphries, Grant; Magness, Dawn R.; Huettmann, Falk, eds. Machine Learning for Ecology and Sustainable Natural Resource Management. Switzerland: Springer Nature: 123-139. Chapter 6. https://doi.org/10.1007/978-3-319-96978-7_6.
