Integration of cluster analysis and granular computing for imbalanced data classification: A case study on prostate cancer prognosis in Taiwan-Reference-Cited by-同舟云学术

Integration of cluster analysis and granular computing for imbalanced data classification: A case study on prostate cancer prognosis in Taiwan

Published:2017-02-24 Issue:3 Volume:32 Page:2251-2267
ISSN:1064-1246
Container-title:Journal of Intelligent & Fuzzy Systems
language:
Short-container-title:IFS

Author:

Kuo R.J.¹,Lin L.²,Zulvia F.E.¹,Lin C.C.³

Affiliation:

1. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan

2. Gemtek Technology Co., Ltd., Zhonghe District, New Taipei City, Taiwan

3. Department of Surgery and Department of Urology, School of Medicine, National Yang-Ming University, Taipei, Taiwan

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference40 articles.

1. A study of the behavior of several methods for balancing machine learning training data;Batista;ACM Sigkdd Explorations Newsletter,2004

2. SMOTE: Synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research,2002

3. Neuro-fuzzy classification of prostate cancer using NEFCLASS-J;Keles;Computers in Biology and Medicine,2007

4. High grade prostatic intraepithelial neoplasia (HGPIN) and prostatic adenocarcinoma between the ages of 20-69: An autopsy study of 249 cases;Sakr;In Vivo,1993

5. Progress of molecular targeted therapies for prostate cancers;Fu;Biochimica et Biophysica Acta (BBA) - Reviews on Cancer,2012

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