An approach for classification of highly imbalanced data using weighting and undersampling-Reference-Cited by-同舟云学术

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An approach for classification of highly imbalanced data using weighting and undersampling

Published:2010-04-22 Issue:5 Volume:39 Page:1385-1391
ISSN:0939-4451
Container-title:Amino Acids
language:en
Short-container-title:Amino Acids

Author:

Anand Ashish,Pugalenthi Ganesan,Fogel Gary B.,Suganthan P. N.

Publisher

Springer Science and Business Media LLC

Subject

Organic Chemistry,Clinical Biochemistry,Biochemistry

Link

http://link.springer.com/content/pdf/10.1007/s00726-010-0595-2.pdf

Reference39 articles.

1. Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. Lect Notes Comput Sci 3201:39–50

2. Batuwita R, Palade V (2009a) microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 25:989–995

3. Batuwita R, Palade V (2009b) AGm: a new performance measure for class imbalance learning. Application to bioinformatics problems. In: Proceedings of 8th international conference on machine learning and applications, ICMLA 2009, 13–15 December 2009, Miami Beach, USA

4. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucl Acids Res 28:235–242

5. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines, 2001, Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

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