Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Creation of Classifier Ensembles for Handwritten Word Recognition Using Feature Selection Algorithms
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
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In constructing a classifier ensemble diversity is more important as the accuracy of its elements. To reach a diverse ensemble, one approach is to produce a pool of classifiers. Then we define a metric to evaluate the diversity value in a set of classifiers. We extract a subset of classifiers out of the pool in such a way that has a high diversity value. Usage of Bagging and Boosting as the sources of generators of diversity is another alternative. The third alternative is to partition classifiers and then select a classifier from each partition. Because of high similarity between classifiers of each partition, there is no need to let more than exactly one classifier from each of partition participate in the final ensemble. In this article, the performance of proposed framework is evaluated on some real datasets of UCI repository. Achieved results show effectiveness of the algorithm compare to the original bagging and boosting algorithms.