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Feature Selection and the Preservation of Infrequent and Highly Significant Attributes in the Context of Arabic Text Mining
by
Saeed Raheel
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 106 - Number 3 |
| Year of Publication: 2014 |
| Authors: Saeed Raheel |
10.5120/18503-9572
|
Saeed Raheel . Feature Selection and the Preservation of Infrequent and Highly Significant Attributes in the Context of Arabic Text Mining. International Journal of Computer Applications. 106, 3 ( November 2014), 31-36. DOI=10.5120/18503-9572
@article{
10.5120/18503-9572,
author = {
Saeed Raheel
},
title = { Feature Selection and the Preservation of Infrequent and Highly Significant Attributes in the Context of Arabic Text Mining },
journal = {
International Journal of Computer Applications
},
issue_date = { November 2014 },
volume = { 106 },
number = { 3 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = {
31-36
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume106/number3/18503-9572/
},
doi = { 10.5120/18503-9572 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:27.049317+05:30
%A Saeed Raheel
%T Feature Selection and the Preservation of Infrequent and Highly Significant Attributes in the Context of Arabic Text Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 3
%P 31-36
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Effective feature selection is a key component for building an efficient automatic document classifier. We regularly encounter in the Arabic literature- especially the scientific one- infrequent non-Arabic words that are eliminated by practice during the pre-processing phase. Although infrequent, those words are highly pertinent to their documents and, thus, can contribute to build a more efficient classification model and enforce the subjectivity of the decision taken by the classifier. Therefore, we propose in this paper four different feature selection solutions that allow both preserving a maximum number of those words and getting satisfactory classification accuracy.
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Index Terms
Computer Science
Information Sciences
Keywords