Machine Learning
Tree-based classifiers and GIS for biological risk forecasting
Advances in intelligent systems
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Boosting and Classification of Electronic Nose Data
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
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Boosting of tree-based classifiers has been interfaced to the Geographical Information System (GIS) GRASS to create predictive classification models from digital maps. On a risk management problem in landscape ecology, the performance of the boosted tree model is better than either with a single classifier or with bagging. This results in an improved digital map of the risk of human exposure to tick-borne diseases in Trentino (Italian Alps) given sampling on 388 sites and the use of several overlaying georeferenced data bases. Margin distributions are compared for bagging and boosting. Boosting is confirmed to give the most accurate model on two additional and independent test sets of reported cases of bites on humans and of infestation measured on roe deer. An interesting feature of combining classification models within a GIS is the visualization through maps of the single elements of the combination: each boosting step map focuses on different details of data distribution. In this problem, the best performance is obtained without controlling tree sizes, which indicates that there is a strong interaction between input variables.