Integration and Selection of Linear SVM Classifiers in Geometric Space
Integration or fusion of the base classifiers is the final stage of creating multiple classifiers system. Known methods in this step use base classifier outputs, which are class labels or values of the confidence (predicted probabilities) for each class label. In this paper we propose an integration...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Graz University of Technology
2019-06-01
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| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/22623/download/pdf/ |
| Summary: | Integration or fusion of the base classifiers is the final stage of creating multiple classifiers system. Known methods in this step use base classifier outputs, which are class labels or values of the confidence (predicted probabilities) for each class label. In this paper we propose an integration process which takes place in the geometric space. It means that the fusion of base classifiers is done using their decision boundaries. In order to obtain one decision boundary from boundaries defined by base classifiers the median or weighted average method will be used. In addition, the proposed algorithm uses the division of the entire feature space into disjoint regions of competence as well as the process of selection of base classifiers is carried out. The aim of the experiments was to compare the proposed algorithms with the majority voting method and assessment which of the analyzed approaches to integration of the base classifiers creates a more effective ensemble. |
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| ISSN: | 0948-6968 |