Open AccessText Classification Based on Weighted Extreme Learning Machine
Author(s) -
Hayder Mahmood Salman
Publication year - 2019
Publication title -
mağallaẗ ibn al-haytam li-l-ʻulūm al-ṣirfaẗ wa-al-taṭbīqiyyaẗ/ibn al-haitham journal for pure and applied sciences
Language(s) - English
Resource type - Journals
eISSN - 2521-3407
pISSN - 1609-4042
DOI - 10.30526/32.1.1978
Subject(s) - extreme learning machine , preprocessor , computer science , artificial intelligence , feature (linguistics) , feature extraction , machine learning , the internet , pattern recognition (psychology) , artificial neural network , linguistics , philosophy , world wide web
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.