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Text classification based on weighted extreme learning machine
Author
Source
Ibn al-Haitham Journal for Pure and Applied Science
Issue
Vol. 32, Issue 1 (30 Apr. 2019), pp.197-204, 8 p.
Publisher
University of Baghdad College of Education for Pure Science / Ibn al-Haitham
Publication Date
2019-04-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Mathematics
Information Technology and Computer Science
Topics
Abstract EN
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.
American Psychological Association (APA)
Salman, Haydar Mahmud. 2019. Text classification based on weighted extreme learning machine. Ibn al-Haitham Journal for Pure and Applied Science،Vol. 32, no. 1, pp.197-204.
https://search.emarefa.net/detail/BIM-898144
Modern Language Association (MLA)
Salman, Haydar Mahmud. Text classification based on weighted extreme learning machine. Ibn al-Haitham Journal for Pure and Applied Science Vol. 32, no. 1 (2019), pp.197-204.
https://search.emarefa.net/detail/BIM-898144
American Medical Association (AMA)
Salman, Haydar Mahmud. Text classification based on weighted extreme learning machine. Ibn al-Haitham Journal for Pure and Applied Science. 2019. Vol. 32, no. 1, pp.197-204.
https://search.emarefa.net/detail/BIM-898144
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 204
Record ID
BIM-898144