Automatic Folder Allocation System for Electronic Text Document Repositories Using Enhanced Bayesian Classification Approach

Wou Onn Choo, Lam Hong Lee, Yen Pei Tay, Khang Wen Goh, Dino Isa and Suliman Mohamed Fati
Additional contact information
Wou Onn Choo: Faulty of Information Technology and Sciences, INTI International University, Nilai, Malaysia
Lam Hong Lee: School of Computing, Faculty of Science and Technology, Quest International University Perak, Ipoh, Malaysia
Yen Pei Tay: School of Computing, Faculty of Science and Technology, Quest International University Perak, Ipoh, Malaysia
Khang Wen Goh: School of Computing, Faculty of Science and Technology, Quest International University Perak, Ipoh, Malaysia
Dino Isa: Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Nottingham, Semenyih, Malaysia
Suliman Mohamed Fati: INTI International University, Nilai, Malaysia

International Journal of Intelligent Information Technologies (IJIIT), 2019, vol. 15, issue 2, 1-19

Abstract: This article proposes a system equipped with the enhanced Bayesian classification techniques to automatically assign folders to store electronic text documents. Despite computer technology advancements in the information age where electronic text files are so pervasive in information exchange, almost every single document created or downloaded from the Internet requires manual classification by the users before being deposited into a folder in a computer. Not only does such a tedious task cause inconvenience to users, the time taken to repeatedly classify and allocate a folder for each text document impedes productivity, especially when dealing with a huge number of files and deep layers of folders. In order to overcome this, a prototype system is built to evaluate the performance of the enhanced Bayesian text classifier for automatic folder allocation, by categorizing text documents based on the existing types of text documents and folders present in user's hard drive. In this article, the authors deploy a High Relevance Keyword Extraction (HRKE) technique and an Automatic Computed Document Dependent (ACDD) Weighting Factor technique to a Bayesian classifier in order to obtain better classification accuracy, while maintaining the low training cost and simple classifying processes using the conventional Bayesian approach.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://services.igi-global.com/resolvedoi/resolve ... 018/IJIIT.2019040101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:15:y:2019:i:2:p:1-19

Access Statistics for this article

International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran

More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().