Machine Learning Classifiers: A Brief Primer

  • Abdul Ahad Abro Ege University, Turkey
  • Abdullah Ayub Khan Benazir Bhutto Shaheed University Lyari, Karachi
  • Mir Sajjad Hussain Talpur Sindh Agriculture University, Tandojam
  • Idrissa Kayijuka Department of Applied Status, University of Rwanda, Rwanda
  • Erkan Yaşar Department of Computer Engineering, Ege University, Izmir, Turkey
Keywords: Artificial Intelligence, Machine Learning, Data Mining, Classification, Knowledge Discovery in Databases


Machine learning is a prominent and an intensively studied field in the artificial intelligence area which assists to enhance the performance of classification. In this paper, the main idea is to provide the classification and comparative analysis of data mining algorithms. To support this idea, six supervised machine learning (ML) algorithms, C4.5 (J48), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and One Rule (OneR) along with the five UCI Datasets of ML Repository, are being applied that demonstrates the robustness and effectiveness of numerous approaches. Whereas, for analytical procedures, significant parameters have been considered: Accuracy, Area Under Curve (AUC), precision, recall, and F-measure values. Hence, the primary objective of this study is to obtain binary classification and efficiency by conducting the performance evaluation. We present experimental results that demonstrate the effectiveness of our approach to well-known competitive approaches.

How to Cite
Abro, A. A., Khan, A. A., Talpur, M. S. H., Idrissa Kayijuka, & Erkan Yaşar. (2021). Machine Learning Classifiers: A Brief Primer. University of Sindh Journal of Information and Communication Technology , 5(2), 63-68. Retrieved from

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