Using Directed Acyclic Graph Support Vector Machines with Tabu Search for Classifying Faulty Product Types

  • Ping-Feng Pai
  • Yu-Ying Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Diagnosing quality faults is one of the most crucial issues in manufacturing processes. Many techniques have been presented to diagnose fault in manufacturing systems. The SVM approach has received more attention due to its classification ability. However, the development of support vector machines (SVM) in the diagnosis of manufacturing systems is rare. Therefore, this investigation attempts to apply the SVM in the diagnosis of manufacturing systems. Furthermore, the tabu search is employed to determine two parameters SVM model correctly and efficiently. A numerical example in the previous literature was used to demonstrate the diagnosis ability of the proposed DSVMT (directed acyclic graph support vector machines with tabu search) model. The experiment results show that the proposed approach can classify the faulty product types correctly.


Support Vector Machine Tabu Search Support Vector Machine Model Tabu List Tabu Search Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ping-Feng Pai
    • 1
  • Yu-Ying Huang
    • 2
  1. 1.Department of Information ManagementNational Chi Nan UniversityNantouTaiwan
  2. 2.Department of Industrial Engineering and Technology ManagementDa-Yeh UniversityChang-huaTaiwan

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