Text Summarization versus CHI for Feature Selection
Journal of Advances in Mathematics and Computer Science · pp. 1–8 · Published 6 Jun 2017
10.9734/BJMCS/2017/33615Abstract
Text Classification is an important technique for handling the huge and increasing amount of text documents on the web. An important problem of text classification is features selection. Many feature selection techniques were used in order to solve this problem, such as chi-square (CHI). Rather than using these techniques, this paper proposes a method for feature selection based on text summarization. We demonstrate this method on Arabic text documents and use text summarization for feature selection. Support Vector Machine (SVM) is then used to classify the summarized documents and the ones processed by CHI. The classification indicators (precision, recall, and accuracy) achieved by text summarization are higher than the ones achieved by CHI. However, text summarization has negligible higher execution time.
References (13)
- 1 A Comparative Study on Feature Selection in Text Categorization
- 2 Text Classification Using Machine Learning Techniques
- 3 Chi Square Feature Extraction Based Svms Arabic Language Text Categorization System [DOI]
- 4 Comparative evaluation of text classification techniques using a large diverse Arabic dataset [DOI]
- 5 Advances in Computer and Information Sciences and Engineering [DOI]
- 6 High-performing feature selection for text classification [DOI]
- 7 Empirical Study on Filter based Feature Selection Methods for Text Classification [DOI]
- 8 VIDEO CLASSIFICATION AND RETRIEVAL USING ARABIC CLOSED CAPTION
- 9 Using ambiguity measure feature selection algorithm for support vector machine classifier [DOI]
- 10 Summarization as Feature Selection for Document Categorization on Small Datasets [DOI]
- 11 Reduction Techniques for Instance Based Text Categorization [DOI]
- 12 INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS [DOI]
- 13 Proceedings of the 1996 ACM symposium on Applied Computing
Cited by 2
A. Ibrahim, Marco Alfonse, M. Aref · International Conference on the Internet, Cyber Security and Information Systems · 2023
Showing 1 of 2 known citations — external sources report more than can currently be individually listed.
Related research
- Arabic Text Summarization Using Latent Semantic Analysis — shares topic coverage
- A Novel Approach to Text Summarization Using Machine Learning — shares topic coverage
- Centroid-Based and Bayesian Algorithms Performance — shares topic coverage
- Using NLP to Enhance Supply Chain Management Systems — shares topic coverage
- Multiclass Text Classification of Climate Change Reports: Insights into Natural Disasters, Impacts, Locations, and Time Periods — shares topic coverage
Article metrics
Real usage data collected on this platform.
32
Page views
0
PDF downloads
5
Outbound clicks
2
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.