The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classi.cation of Examples by Multiple Agents with Private Features
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
The main prerequisite for the efficient use of ensemble systems is that the base classifiers should be diverse among themselves. One way of increasing diversity is through the use of feature distribution methods in ensemble systems. In this paper, an investigation of the use of feature distribution methods among the classifiers of ensemble systems will be performed. In this investigation, five different methods of data distribution will be used. These ensemble systems will use six existing combination methods, in which four of them are fusion-based methods and the remaining two are selection-based methods. As a result, it is aimed to detect which ensemble systems are more suitable to use feature distribution among the classifier.