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

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.

Fault Diagnosis of Airborne Electronic Equipment Based on Dynamic Bayesian Networks

“…Therefore, the fault text information must be characterized and preprocessed to extract the metadata that can reflect the characteristics of each fault. The extracted feature information is automatically assigned to folders for storing electronic text information using a system with enhanced Bayesian classification techniques (Choo et al, 2019). Based on the airborne electronic equipment fault data table, the types and cause nodes that can reflect the fault symptoms are extracted based on the frequency and importance of fault vocabulary in the fault text information, as shown in Tables 1 and 2.…”