Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.]
Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Universiti Teknologi MARA
2024
|
Subjects: | |
Online Access: | https://ir.uitm.edu.my/id/eprint/61957/1/61957.pdf https://ir.uitm.edu.my/id/eprint/61957/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Universiti Teknologi Mara |
Language: | English |
id |
my.uitm.ir.61957 |
---|---|
record_format |
eprints |
spelling |
my.uitm.ir.619572024-04-18T08:44:08Z https://ir.uitm.edu.my/id/eprint/61957/ Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] mjoc Bohani, Farah Aqilah Mohamed Rashid, Farah Syazwani Mahmud, Yuzi Yahya, Sitti Rachmawati Air transportation. Airlines Consumer satisfaction Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of feature selection has been analyzed based on the accuracy of two classifiers: J48 and Naïve Bayes. The Airline Customer Satisfaction datasets have been used for comparing with and without applying Information Gain. As a result, J48 achieved 0.33% and 0.29% improvements in accuracy after applying Information Gain for 10-fold and 20-fold cross-validation, respectively compared to Naïve Bayes. Most of the precision and F1-score for J48 with Information Gain have also improved for both evaluation methods compared to Naïve Bayes. In conclusion, J48 seems to be the classifier that is most sensitive to feature selection and has shown improvements compared to Naïve Bayes. Universiti Teknologi MARA 2024-04 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/61957/1/61957.pdf Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.]. (2024) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29/>, 9 (1): 2. pp. 1673-1689. ISSN 2600-8238 |
institution |
Universiti Teknologi Mara |
building |
Tun Abdul Razak Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Mara |
content_source |
UiTM Institutional Repository |
url_provider |
http://ir.uitm.edu.my/ |
language |
English |
topic |
Air transportation. Airlines Consumer satisfaction |
spellingShingle |
Air transportation. Airlines Consumer satisfaction Bohani, Farah Aqilah Mohamed Rashid, Farah Syazwani Mahmud, Yuzi Yahya, Sitti Rachmawati Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] |
description |
Feature selection has become a focus of research in many fields that deal with machine learning and data mining because it makes classifiers cost-effective, faster, and more accurate. In this paper, the impact of feature selection using filter methods such as Information Gain is shown. The impact of feature selection has been analyzed based on the accuracy of two classifiers: J48 and Naïve Bayes. The Airline Customer Satisfaction datasets have been used for comparing with and without applying Information Gain. As a result, J48 achieved 0.33% and 0.29% improvements in accuracy after applying Information Gain for 10-fold and 20-fold cross-validation, respectively compared to Naïve Bayes. Most of the precision and F1-score for J48 with Information Gain have also improved for both evaluation methods compared to Naïve Bayes. In conclusion, J48 seems to be the classifier that is most sensitive to feature selection and has shown improvements compared to Naïve Bayes. |
format |
Article |
author |
Bohani, Farah Aqilah Mohamed Rashid, Farah Syazwani Mahmud, Yuzi Yahya, Sitti Rachmawati |
author_facet |
Bohani, Farah Aqilah Mohamed Rashid, Farah Syazwani Mahmud, Yuzi Yahya, Sitti Rachmawati |
author_sort |
Bohani, Farah Aqilah |
title |
Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] |
title_short |
Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] |
title_full |
Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] |
title_fullStr |
Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] |
title_full_unstemmed |
Analyzing the impact of feature selection using information gain for airlines' customer satisfaction / Farah Aqilah Bohani ... [et al.] |
title_sort |
analyzing the impact of feature selection using information gain for airlines' customer satisfaction / farah aqilah bohani ... [et al.] |
publisher |
Universiti Teknologi MARA |
publishDate |
2024 |
url |
https://ir.uitm.edu.my/id/eprint/61957/1/61957.pdf https://ir.uitm.edu.my/id/eprint/61957/ |
_version_ |
1797924680045166592 |