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Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm
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DOI：10.1016/j.ejor.2018.11.024.png)
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Abstract
En 中文
Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Machine (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 propose 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 effectiveness of our approach. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Data mining
Functional Data classification
Parameter tuning
SVM
Functional bandwidth
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