Abstract
The data set of forest cover types based on cartographic data consists of very large data set of 581,102 instances. So, decision tree-based data mining methods that need relatively less computing resources could be used for better classification models. Random forests consisting of multitude of special decision trees are known to be a good data mining tool, and a technique based on grid search of random forests was investigated to find very accurate classifier. Experiments showed that a classifier of high accuracy could be found for the data set of forest cover types.
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Sug, H. (2014). Better Induction Models for Classification of Forest Cover. In: Park, J., Adeli, H., Park, N., Woungang, I. (eds) Mobile, Ubiquitous, and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40675-1_29
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DOI: https://doi.org/10.1007/978-3-642-40675-1_29
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