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Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach

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Abstract

This paper proposes an automatic folder allocation system for text documents through the implementation of a hybrid classification method which combines the Bayesian (Bayes) approach and the Support Vector Machines (SVMs). Folder allocation for text documents in computer is typically executed manually by the user. Every time the user creates text documents by using text editors or downloads the documents from the internet, and wishes to store these documents on the computer, the user needs to determine and allocate the appropriate folder in which to store these new documents. This situation is inconvenient as repeating the folder allocation each time a text document is stored becomes tedious especially when the numbers and layers of folders are huge and the structure is complex and continuously growing. This problem can be overcome by implementing Artificial Intelligence machine learning methods to classify the new text documents and allocate the most appropriate folder as the storage for them. In this paper we propose the Bayes-SVMs hybrid classification framework to perform the tedious task of automatically allocating the right folder for text documents in computers.

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Authors and Affiliations

  1. Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900, Kampar, Perak, Malaysia

    Lam Hong Lee

  2. Intelligent Systems Research Group, Faculty of Engineering, The University of Nottingham, Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia

    Rajprasad Rajkumar & Dino Isa

Authors
  1. Lam Hong Lee
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  2. Rajprasad Rajkumar
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  3. Dino Isa
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Correspondence to Lam Hong Lee.

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Lee, L.H., Rajkumar, R. & Isa, D. Automatic folder allocation system using Bayesian-support vector machines hybrid classification approach. Appl Intell 36, 295–307 (2012). https://doi.org/10.1007/s10489-010-0261-0

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