Open AccessResearch on Server Health State Prediction Model Based on Support Vector Machine
Author(s) -
Dan Jin,
Ce Li,
Qiong Wang,
Yuhang Chen
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012029
Subject(s) - computer science , support vector machine , artificial intelligence , machine learning , generalization , feature selection , feature vector , data mining , classifier (uml) , relevance vector machine , structured support vector machine , mathematics , mathematical analysis
With the development of artificial intelligence, machine learning methods have been widely applied to predictive modelling in various fields. Support vector machine is a linear classifier with the largest interval defined on the feature space. In order to improve the current status of server operation and maintenance, and improve the accuracy and efficiency of server health prediction, this paper studies the feature quantity selection based on grey relational analysis, studies the support vector machine applied to data generalization and classification, and establishes a health prediction model. The simulation training of historical data shows that the method has fast learning, high prediction speed, high efficiency, high accuracy and wide application prospects.