Abstract
The paper presents an empirical study aiming at evaluating and comparing several Machine Learning (ML) classification techniques in the automatic recognition of known patterns. The main motivations of this work is to select best performing classification techniques where target classes are based on the occurrence of known patterns in configurations of a forest system modeled according to Cellular Automata. Best performing ML classifiers will be adopted for the study of ecosystem dynamics within an interdisciplinary research collaboration between computer scientists, biologists and ecosystem managers (Cellular Automata For Forest Ecosystems – CAFFE project). One of the main aims of the CAFFE project is the development of an analysis method based on recognition in CA state configurations of spatial patterns whose interpretations are inspired by the Chinese Go game.
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Bandini, S., Manzoni, S., Redaelli, S., Vanneschi, L. (2006). Automatic Detection of Go–Based Patterns in CA Model of Vegetable Populations: Experiments on Geta Pattern Recognition. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds) Cellular Automata. ACRI 2006. Lecture Notes in Computer Science, vol 4173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861201_50
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DOI: https://doi.org/10.1007/11861201_50
Publisher Name: Springer, Berlin, Heidelberg
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