Functional-bandwidth kernel for Support Vector Machine with Functional Data_An alternating optimization algorithm
Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Ma- chine (SVM) is a benchmark tool for classification, in particular, of functional data. SVM is frequently used with a kernel (e.g.: Gaussian) which involves a scalar bandwidth parameter. In this pap...
| Autores: | , , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2018 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/107638 |
| Acesso em linha: | https://hdl.handle.net/11441/107638 https://doi.org/10.1016/j.ejor.2018.11.024 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Data mining Functional Data classification Parameter tuning SVM Functional bandwidth |
| Resumo: | Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Ma- chine (SVM) is a benchmark tool for classification, in particular, of functional data. SVM is frequently used with a kernel (e.g.: Gaussian) which involves a scalar bandwidth parameter. In this paper, we pro- pose to use kernels with functional bandwidths. In this way, accuracy may be improved, and the time intervals critical for classification are identified. Tuning the functional parameters of the new kernel is a challenging task expressed as a continuous optimization problem, solved by means of a heuristic. Our experiments with benchmark data sets show the advantages of using functional parameters and the ef- fectiveness of our approach. |
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