Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Using diversity of errors for selecting members of a committee classifier
Pattern Recognition
Confidence based multiple classifier fusion in speaker verification
Pattern Recognition Letters
3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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In this paper, a new probabilistic model using measures of classifier competence and diversity is proposed. The multiple classifier system (MCS) based on the dynamic ensemble selection scheme was constructed using both developed measures. Two different optimization problems of ensemble selection are defined and a solution based on the simulated annealing algorithm is presented. The influence of minimum value of competence and diversity in the ensemble on classification performance was investigated. The effectiveness of the proposed dynamic selection methods and the influence of both measures were tested using seven databases taken from the UCI Machine Learning Repository and the StatLib statistical dataset. Two types of ensembles were used: homogeneous or heterogeneous. The results show that the use of diversity positively affects the quality of classification. In addition, cases have been identified in which the use of this measure has the greatest impact on quality.