Open AccessRapidMiner and Machine Learning Techniques for Classifying Aircraft Data
Author(s) -
Syahaneim Marzukhi,
Norfatimah Awang,
Syed Nasir Alsagoff,
Hassan Mohamed
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1997/1/012012
Subject(s) - machine learning , artificial intelligence , decision tree , computer science , random forest , naive bayes classifier , id3 algorithm , cluster analysis , id3 , task (project management) , data mining , decision tree learning , incremental decision tree , support vector machine , engineering , systems engineering
Machine learning is an important technique that helps companies, organizations and individuals to improve the quality of decision making. In today scenario, especially with the emerged of data science, it can see how machine learning techniques are used for data analytics. There are various machine learning techniques for data science tasks that can be categorized as follows: classification, prediction, regression, association analysis, clustering, time series forecasting, and many others. As there are many different free tools available for machine learning, the selection of the appropriate analysis technique is crucial to solve problem in hand. This study compares the performance of machine learning algorithms especially Naïve Bayes, Decision Tree, Random Forest and ID3 for classification task (i.e. classifying aircraft to certain category and into country of origin) using RapidMiner tool. Those algorithms are compared based on their accuracy rate, error rates, precision and recall for classifying aircraft. The results reveal that that Random Forest and ID3 algorithms given good classification accuracy due to the nature of the algorithms that is progressively improved apart from Decision Tree.