IAES International Journal of Artificial Intelligence (IJ-AI)
Vol 13, No 1: March 2024

Machine learning for text document classification-efficient classification approach

Mohammed Ali, Sura I. (Unknown)
Nihad, Marwah (Unknown)
Mohamed Sharaf, Hussien (Unknown)
Farouk, Haitham (Unknown)



Article Info

Publish Date
01 Mar 2024

Abstract

Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.

Copyrights © 2024






Journal Info

Abbrev

IJAI

Publisher

Subject

Computer Science & IT Engineering

Description

IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like ...