A kernel machine for hidden object-ranking problems (HORPs)
| Autor: | Gaël Mondonneix, Jean Martial Mari, Sébastien Chabrier, Alban Gabillon |
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| Přispěvatelé: | Géopôle du Pacifique Sud (GePaSUD), Université de la Polynésie Française (UPF) |
| Rok vydání: | 2020 |
| Předmět: |
Kernel machine
Support vector machine Computer Networks and Communications Computer science Learning bias Feature selection 02 engineering and technology 01 natural sciences 010104 statistics & probability Kernel (linear algebra) 0202 electrical engineering electronic engineering information engineering Media Technology [INFO]Computer Science [cs] 0101 mathematics Ordinal learning business.industry Pearls quality assessment Pattern recognition Classification Object (computer science) Object-ranking Kernel method Ranking Instance-ranking Hardware and Architecture Kernel (statistics) 020201 artificial intelligence & image processing Data pre-processing Artificial intelligence business Software |
| Zdroj: | Multimedia Tools and Applications Multimedia Tools and Applications, Springer Verlag, 2020, 79 (47-48), pp.35093-35107. ⟨10.1007/s11042-020-09184-y⟩ |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-020-09184-y |
| Popis: | Hidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clustered. We propose a kernel machine that allows retaining item-related ordinal information while avoiding emphasizing class-related information. We show how this kernel machine can be implemented with standard optimization libraries provided slight modifications on the original kernel. The proposed approach is tested on Tahitian pearls quality assessment and compared with four other classical methods. It yields better results (93.6% ± 3.9% of correct predictions without feature selection, 94.3% ± 3.4% with feature selection) than the best of the other tested methods (91.3% ± 3.4% and 92.6% ± 4.3% without and with feature selection for the instance-ranking approach), this improvement being significant (p-value |
| Databáze: | OpenAIRE |
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