LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data-Reference-Cited by-同舟云学术

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LVQ-SMOTE – Learning Vector Quantization based Synthetic Minority Over–sampling Technique for biomedical data

Published:2013-10-02 Issue:1 Volume:6 Page:
ISSN:1756-0381
Container-title:BioData Mining
language:en
Short-container-title:BioData Mining

Author:

Nakamura Munehiro,Kajiwara Yusuke,Otsuka Atsushi,Kimura Haruhiko

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Genetics,Molecular Biology,Biochemistry

Link

http://link.springer.com/article/10.1186/1756-0381-6-16/fulltext.html

Reference26 articles.

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2. Yu C, Chou L, Chang D: Predicting protein-protein interactions in unbalanced data using the primary structure of proteins. BMC Bioinformatics. 2010, 11 (167): 1-10.

3. Haibo H: Learning from imbalanced data. IEEE Trans Knowledge Data Eng. 2009, 21 (9): 1263-1284.

4. Freund Y: Boosting a weak learning algorithm by majority. Inform Comput. 1995, 121 (2): 256-285. 10.1006/inco.1995.1136.

5. Quinlan R: C4.5: Proggrams for Machine Learning. 1993, San Francisco: Morgan Kaufmann Publishers

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