On the Optimum Linear Soft Fusion of Classifiers
We present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error i...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/9/5038 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850278667533418496 |
|---|---|
| author | Luis Vergara Addisson Salazar |
| author_facet | Luis Vergara Addisson Salazar |
| author_sort | Luis Vergara |
| collection | DOAJ |
| description | We present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error improvement factor relative to the best fused classifier. Key elements for this improvement factor are the number of classifiers, their pairwise correlations, the imbalance between their performances, and the bias. Furthermore, we consider exponential models for the class-conditional probability densities to establish the relationship between the classifier’s error probability and the mean square error of the class estimate. This allows us to predict the reduction in the post-fusion error probability relative to that of the best classifier. These theoretical findings are contrasted in a biosignal application for the detection of arousals during sleep from EEG signals. The results obtained are reasonably consistent with the theoretical conclusions. |
| format | Article |
| id | doaj-art-060c6bf4fe834ce592932fee9f09bb77 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-060c6bf4fe834ce592932fee9f09bb772025-08-20T01:49:24ZengMDPI AGApplied Sciences2076-34172025-05-01159503810.3390/app15095038On the Optimum Linear Soft Fusion of ClassifiersLuis Vergara0Addisson Salazar1Instituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, C/Camino de Vera s/n, 46022 València, SpainInstituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, C/Camino de Vera s/n, 46022 València, SpainWe present new analytical developments that contribute to a better understanding of the (soft) fusion of classifiers. To this end, we propose an optimal linear combiner based on a minimum mean-square-error class estimation approach. This solution allows us to define a post-fusion mean-square-error improvement factor relative to the best fused classifier. Key elements for this improvement factor are the number of classifiers, their pairwise correlations, the imbalance between their performances, and the bias. Furthermore, we consider exponential models for the class-conditional probability densities to establish the relationship between the classifier’s error probability and the mean square error of the class estimate. This allows us to predict the reduction in the post-fusion error probability relative to that of the best classifier. These theoretical findings are contrasted in a biosignal application for the detection of arousals during sleep from EEG signals. The results obtained are reasonably consistent with the theoretical conclusions.https://www.mdpi.com/2076-3417/15/9/5038classifierssoft fusionoptimal combinerexpected improvementfusion of biosignals |
| spellingShingle | Luis Vergara Addisson Salazar On the Optimum Linear Soft Fusion of Classifiers Applied Sciences classifiers soft fusion optimal combiner expected improvement fusion of biosignals |
| title | On the Optimum Linear Soft Fusion of Classifiers |
| title_full | On the Optimum Linear Soft Fusion of Classifiers |
| title_fullStr | On the Optimum Linear Soft Fusion of Classifiers |
| title_full_unstemmed | On the Optimum Linear Soft Fusion of Classifiers |
| title_short | On the Optimum Linear Soft Fusion of Classifiers |
| title_sort | on the optimum linear soft fusion of classifiers |
| topic | classifiers soft fusion optimal combiner expected improvement fusion of biosignals |
| url | https://www.mdpi.com/2076-3417/15/9/5038 |
| work_keys_str_mv | AT luisvergara ontheoptimumlinearsoftfusionofclassifiers AT addissonsalazar ontheoptimumlinearsoftfusionofclassifiers |