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Performance comparison of machine learning classifiers on aircraft databases

The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better...

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Main Authors: Kamarudin, Nur Diyana, Rahayu, Syarifah Bahiyah, Zainol, Zuraini, Rusli, Mohd. Shahrizal, Abdul Ghani, Kamaruddin
Format: Article
Published: Science and Technology Research Institute for Defence 2018
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/86570/
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spelling my.utm.865702020-09-30T08:43:42Z http://eprints.utm.my/id/eprint/86570/ Performance comparison of machine learning classifiers on aircraft databases Kamarudin, Nur Diyana Rahayu, Syarifah Bahiyah Zainol, Zuraini Rusli, Mohd. Shahrizal Abdul Ghani, Kamaruddin QA75 Electronic computers. Computer science The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better accuracy. The six algorithms are implemented to classify aircraft type and its country of origin using a Waikato Environment for Knowledge Analysis (WEKA) workbench. Additionally, we report our parameter optimisation results for SMO by varying the cost parameters to obtain the optimum result. It is observed that in both classifications, SMO with linear kernel obtained the best performance as compared to the other classifiers in terms of classification accuracy, which is 100%. Science and Technology Research Institute for Defence 2018 Article PeerReviewed Kamarudin, Nur Diyana and Rahayu, Syarifah Bahiyah and Zainol, Zuraini and Rusli, Mohd. Shahrizal and Abdul Ghani, Kamaruddin (2018) Performance comparison of machine learning classifiers on aircraft databases. Defence S and T Technical Bulletin, 11 (2). pp. 154-169. ISSN 1985-6571
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Kamarudin, Nur Diyana
Rahayu, Syarifah Bahiyah
Zainol, Zuraini
Rusli, Mohd. Shahrizal
Abdul Ghani, Kamaruddin
Performance comparison of machine learning classifiers on aircraft databases
description The aim of this research is to analyse the performance of six different classifiers, which are κ-Nearest Neighbours (kNN), Naive Bayes, Random Tree, J48 Decision Tree, Random Forest Tree and Sequential Minimal Optimisation (SMO), using aircraft databases and optimize their cost parameter for better accuracy. The six algorithms are implemented to classify aircraft type and its country of origin using a Waikato Environment for Knowledge Analysis (WEKA) workbench. Additionally, we report our parameter optimisation results for SMO by varying the cost parameters to obtain the optimum result. It is observed that in both classifications, SMO with linear kernel obtained the best performance as compared to the other classifiers in terms of classification accuracy, which is 100%.
format Article
author Kamarudin, Nur Diyana
Rahayu, Syarifah Bahiyah
Zainol, Zuraini
Rusli, Mohd. Shahrizal
Abdul Ghani, Kamaruddin
author_facet Kamarudin, Nur Diyana
Rahayu, Syarifah Bahiyah
Zainol, Zuraini
Rusli, Mohd. Shahrizal
Abdul Ghani, Kamaruddin
author_sort Kamarudin, Nur Diyana
title Performance comparison of machine learning classifiers on aircraft databases
title_short Performance comparison of machine learning classifiers on aircraft databases
title_full Performance comparison of machine learning classifiers on aircraft databases
title_fullStr Performance comparison of machine learning classifiers on aircraft databases
title_full_unstemmed Performance comparison of machine learning classifiers on aircraft databases
title_sort performance comparison of machine learning classifiers on aircraft databases
publisher Science and Technology Research Institute for Defence
publishDate 2018
url http://eprints.utm.my/id/eprint/86570/
_version_ 1680321065399615488
score 13.211869

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