Empirical Comparison of Area under ROC curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating Machine Learning Algorithms on Imbalanced Datasets for Binary Classification-Reference-Cited by-同舟云学术

Empirical Comparison of Area under ROC curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating Machine Learning Algorithms on Imbalanced Datasets for Binary Classification

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 3rd International Conference on Machine Learning and Soft Computing - ICMLSC 2019
language:
Short-container-title:

Author:

Halimu Chongomweru¹,Kasem Asem¹,Newaz S. H. Shah¹

Affiliation:

1. Universiti Teknologi Brunei, School of Computing and Informatics, Jalan Tungku Link Gadong BE

Publisher

ACM Press

Reference21 articles.

1. J. Alcalá-Fdez, A Fernández, J Luengo, J Derrac, S García, L Sánchez, and F Herrera. 2011. KEEL data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. Mult. Log. Soft Comput. 17, 2--3 (2011), 255--287.

2. Sabri Boughorbel, Fethi Jarray, and Mohammed El-Anbari. 2017. Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS One 12, 6 (2017), 1--17.

3. A. P. Bradley. 1997. The use of the area under ROC curve in the evaluation of Machine Learning Algorithms. Pattern Recognit. 30, 7 (1997), 1145--1159.

4. Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recognit. Lett. 27, 8 (2006), 861--874.

5. Guo Haixiang, Li Yijing, Jennifer Shang, Gu Mingyun, Huang Yuanyue, and Gong Bing. 2017. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 73, 220--239.

Cited by 82 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems;Information;2024-08-16

2. GPT-4 as an X data annotator: Unraveling its performance on a stance classification task;PLOS ONE;2024-08-15

3. Predicting non-chemotherapy drug-induced agranulocytosis toxicity through ensemble machine learning approaches;Frontiers in Pharmacology;2024-08-14

4. A template and tutorial for preregistering studies using passive smartphone measures;Behavior Research Methods;2024-08-07

5. Design and validation of an artificial intelligence-powered instrument for the assessment of migraine risk in university students in Lebanon;Computers in Human Behavior Reports;2024-08