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

作者：Lee Lam Hong^*; Rajkumar Rajprasad; Isa Dino

来源：Applied Intelligence, 2012, 36(2): 295-307.

DOI：10.1007/s10489-010-0261-0

• Document classification
• Bayes formula
• Bayesian approach
• Support vector machines
• Automatic folder allocation

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.

• 出版日期2012-3