When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of...
Random forests were introduced as a machine learning tool in Breiman (2001) and have since proven to be very popular and powerful for high-dimensional regression and classificatio...
Many recent applications deal with data streams, conceptually endless sequences of data records, often arriving at high flow rates. Standard data-mining techniques typically assu...
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma...
Abstract—We introduce and validate Spatiotemporal Relational Random Forests, which are random forests created with spatiotemporal relational probability trees. We build on the do...
Timothy A. Supinie, Amy McGovern, John Williams, J...
In the Pattern Recognition field, growing interest has been shown in recent years for Multiple Classifier Systems and particularly for Bagging, Boosting and Random Subspaces. Th...