A Brief Overview on Parameter Optimization of Support Vector Machine

Abstract

Support vector machine (SVM) has been successfully applied in classification and regression problems. But it is very sensitive to the selection of parameters. The fundamental principles of SVM are analyzed firstly. The main optimization methods and achievements for SVM parameters are introduced. And the popular fitness functions used for the parameter optimization of SVM are described. The objective of this paper is to provide readers a brief overview of the recent advances for parameter optimization of SVM and enable them to develop and implement new optimization strategies for SVM-related research at their disposal.

Metaheuristics and Support Vector Data Description for Fault Detection in Industrial Processes

“…The metric under consideration is the well known F1 score (17) due to the fact that this metric takes into account precision and sensitivity. Such metric is computed based on the true and false positives (TP and FP) and negatives (TN and FN) (18) and (19). Moreover, this is highly relevant in the detection of faults in industrial processes.…”

“…Ref. [18] presents a brief general description about hyper-parameter optimization in Support Vector Machines (SVM). In this context, the ant colony algorithm is chosen in [19] for feature selection and parameter optimization in SVM for fault diagnosis.…”