Fuzzy Output Support Vector Machine Based Incident Ticket Classification

Abstract

Incident ticket classification plays an important role in the complex system maintenance. However, low classification accuracy will result in high maintenance costs. To solve this issue, this paper proposes a fuzzy output support vector machine (FOSVM) based incident ticket classification approach, which can be implemented in the context of both two-class SVMs and multi-class SVMs such as one-versus-one and one-versus-rest. Our purpose is to solve the unclassifiable regions of multi-class SVMs to output reliable and robust results by more fine-grained analysis. Experiments on both benchmark data sets and real-world ticket data demonstrate that our method has better performance than commonly used multi-class SVM and fuzzy SVM methods.