Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique-Reference-Cited by-同舟云学术

Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Published:2019 Issue: Volume: Page:99-110
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Mohd Fatihah,Abdul Jalil Masita,Noora Noor Maizura Mohamad,Ismail Suryani,Yahya Wan Fatin Fatihah,Mohamad Mumtazimah

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-36365-9_8

Reference36 articles.

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3. Malof, J.M., Mazurowski, M.A., Tourassi, G.D.: The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support. Neural Netw. 25(1), 141–145 (2012)

4. Wang, Q.: A hybrid sampling SVM approach to imbalanced data classification. Abstr. Appl. Anal. 2014, 1–7 (2014)

5. Borowska, K., Topczewska, M.: Data preprocessing in the classification of the imbalanced data. Adv. Comput. Sci. Res. 11, 31–46 (2014)

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