Text Classification Based on Weighted Extreme Learning Machine

Main Article Content

Hayder Mahmood Salman

Abstract

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

Article Details

How to Cite
SALMAN, Hayder Mahmood. Text Classification Based on Weighted Extreme Learning Machine. Ibn AL- Haitham Journal For Pure and Applied Science, [S.l.], v. 32, n. 1, p. 197-204, jan. 2019. ISSN 2521-3407. Available at: <http://jih.uobaghdad.edu.iq/index.php/j/article/view/1978>. Date accessed: 20 feb. 2019. doi: http://dx.doi.org/10.30526/32.1.1978.
Section
computer