Abstract-The use of bagging is explored to create an ensemble of fuzzy classifiers. The learning algorithm used was ANFIS (Adaptive Neuro-Fuzzy Inference Systems). We compare results from bagging to those of a single classifier using both crisp and fuzzy classifier combination methods. Results on 20 data sets show that bagging results in a significantly more accurate classifier.
We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of a large scale simulation where the volume of the data is such that classifiers can train only on data local to a given partition. As a result of the partition reflecting the need for efficient simulation analysis, rather than the needs of data mining, the class statistics vary across partitions; indeed some classes will likely be absent from some partitions. We combine a fast ensemble learning algorithm with majority voting to generate an accurate working model of the simulation. Results from several simulations show that regions of interest are successfully identified in spite of training set class imbalances. Accuracy is analyzed both at the level of nodes in the simulation data structure, and in terms of higher-level regions of interest. It is shown that over 98% of salient regions are found in independent test sets. Hence, this approach will be a significant time saver for simulation users and developers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.