Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

THE RESEARCH OF KNN AND SVM CLASSIFICATION PERFORMANCE ON TWO KINDS OF UNBALANCED DATA SET

Authors
Juan DU, Li-li JIANG
Corresponding Author
Juan DU
Available Online March 2013.
DOI
10.2991/iccsee.2013.366How to use a DOI?
Keywords
Unbalanced Data Set, classify, KNN, SVM
Abstract

For Unbalanced Data Set, the KNN (K - the nearest neighbor) and SVM (support vector machine) classification algorithm’s prediction result would tend to most class; the misclassification rate of the minority class was big. This paper analyzed in detail the influence of unbalanced data set to KNN and SVM in theory, and proposed a new method to solve this problem. Experiment based on UCI data set using KNN and SVM algorithm to prove the validity of the proposed method.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
10.2991/iccsee.2013.366How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Juan DU
AU  - Li-li JIANG
PY  - 2013/03
DA  - 2013/03
TI  - THE RESEARCH OF KNN AND SVM CLASSIFICATION PERFORMANCE ON TWO KINDS OF UNBALANCED DATA SET
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1453
EP  - 1456
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.366
DO  - 10.2991/iccsee.2013.366
ID  - DU2013/03
ER  -