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Title: Performance comparison of extreme learning machines and other machine learning methods on WBCD data set
Authors: Keskin, O.S.
Durdu, A.
Aslan, M.F.
Yusefi, A.
Keywords: Breast cancer
Extreme learning machines
Machine learning
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Breast cancer is one of the most common forms of cancer among women in our country and the world. Artificial intelligence studies are growing in order to reduce the mortality and early diagnosis needed for appropriate treatment. The Excessive Learning Machines (ELM) method, one of the machine learning approaches, is applied to the Wisconsin Breast Cancer Diagnostic (WBCD) dataset in this study, and the findings are compared to those of other machine learning methods. For this purpose, the same dataset is also classified using Multi-Layer Perceptron (MLP), Sequential Minimum Optimization (SMO), Decision Tree Learning (J48), Naive Bayes (NB), and K-Nearest Neighbor (KNN) methods. According to the results of the study, the ELM approach is more successful than other approaches on the WBCD dataset. It's also worth noting that as the number of neurons in the ELM grows, so does the learning ability of the network. However, after a certain number of neurons have passed, test performance begins to decline sharply. Finally, the ELM's performance is compared to the results of other studies in the literature. © 2021 IEEE.
Description: 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- -- 170536
ISBN: 9781665436496
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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