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Publication
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| Title of Article |
Hybrid balancing technique using GRSOM and bootstap algorithms for classifiers with imbalanced data |
| Date of Acceptance |
1 May 2014 |
| Journal |
| Title of Journal |
Advanced Materials Research |
| Standard |
SCOPUS |
| Institute of Journal |
Scientific.net |
| ISBN/ISSN |
1662-8985 |
| Volume |
2014 |
| Issue |
931-932 |
| Month |
May |
| Year of Publication |
2014 |
| Page |
1375-1381 |
| Abstract |
To deal with imbalanced data, this paper proposes a hybrid data balancing technique
which incorporates both over and under-sampling approaches. This technique determines how much
minority data should be grown as well as how much majority data should be reduced. In this
manner, noise introduced to the data due to excessive over-sampling could be avoided. On top of
that, the proposed data balancing technique helps to determine the appropriate size of the balanced
data and thus computation time required for construction of classifiers would be more efficient. The
data balancing technique over samples the minority data through GRSOM method and then under
samples the majority data using the bootstrap sampling approach. GRSOM is used in this study
because it grows new samples in a non-linear fashion and preserves the original data structure.
Performance of the proposed method is tested using four data sets from UCI Machine Learning
Repository. Once the data sets are balanced, the committee of classifiers is constructed using these
balanced data. The experimental results reveal that our proposed data balancing method provides
the best performance. |
| Keyword |
Imbalanced data, data balancing technique, committee networks |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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