Toward Self-Adaptive Selection of Kernel Functions for Support Vector Regression in IoT-Based Marine Data Prediction
Xiaochuan Sun, Yingqi Li, Ning Wang, Zhigang Li, Miao Liu, Guan Gui
Abstract
Support vector machine (SVM) is a powerful machine learning (ML) technology and the distinctive generalization ability makes it one of the most popular approximation tools in the field of Internet-of-Things (IoT)-based marine data processing. However, SVM has been criticized for trial and error of parameters, especially, kernel function. How to determine a suitable kernel for SVM in a specific problem has been rather tricky. To give a systematic research of the field, we concentrate on the self-adaptive selection of kernel functions in the framework of SVM for IoT-based marine data prediction. Specifically, we adopt the optimal kernel for obtaining competitive SVM and devises a kernel selection criteria of such high-efficiency models. Experiments are conducted via IoT-based real-world marine data sets of different characteristics. The results demonstrate that our proposed self-adaptive SVM model can autonomously provide a suitable kernel for given marine environmental factor prediction, and outperform the alternative with the linear combination of multiple kernels. Besides, the superior performance is verified from the perspective of statistic analysis.