DBMUTE: density-based majority under-sampling technique
© 2016, Springer-Verlag London. Class imbalance is a challenging problem that demonstrates the unsatisfactory classification performance of a minority class. A trivial classifier is biased toward minority instances because of their tiny fraction. In this paper, our density function is defined as the...
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th-cmuir.6653943832-571002018-09-05T03:35:03Z DBMUTE: density-based majority under-sampling technique Chumphol Bunkhumpornpat Krung Sinapiromsaran Computer Science © 2016, Springer-Verlag London. Class imbalance is a challenging problem that demonstrates the unsatisfactory classification performance of a minority class. A trivial classifier is biased toward minority instances because of their tiny fraction. In this paper, our density function is defined as the distance along the shortest path between each majority instance and a minority-cluster pseudo-centroid in an underlying cluster graph. A short path implies highly overlapping dense minority instances. In contrast, a long path indicates a sparsity of instances. A new under-sampling algorithm is proposed to eliminate majority instances with low distances because these instances are insignificant and obscure the classification boundary in the overlapping region. The results show predictive improvements on a minority class from various classifiers on different UCI datasets. 2018-09-05T03:35:03Z 2018-09-05T03:35:03Z 2017-03-01 Journal 02193116 02191377 2-s2.0-84970990065 10.1007/s10115-016-0957-5 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84970990065&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57100 |
| institution |
Chiang Mai University |
| building |
Chiang Mai University Library |
| country |
Thailand |
| collection |
CMU Intellectual Repository |
| topic |
Computer Science |
| spellingShingle |
Computer Science Chumphol Bunkhumpornpat Krung Sinapiromsaran DBMUTE: density-based majority under-sampling technique |
| description |
© 2016, Springer-Verlag London. Class imbalance is a challenging problem that demonstrates the unsatisfactory classification performance of a minority class. A trivial classifier is biased toward minority instances because of their tiny fraction. In this paper, our density function is defined as the distance along the shortest path between each majority instance and a minority-cluster pseudo-centroid in an underlying cluster graph. A short path implies highly overlapping dense minority instances. In contrast, a long path indicates a sparsity of instances. A new under-sampling algorithm is proposed to eliminate majority instances with low distances because these instances are insignificant and obscure the classification boundary in the overlapping region. The results show predictive improvements on a minority class from various classifiers on different UCI datasets. |
| format |
Journal |
| author |
Chumphol Bunkhumpornpat Krung Sinapiromsaran |
| author_facet |
Chumphol Bunkhumpornpat Krung Sinapiromsaran |
| author_sort |
Chumphol Bunkhumpornpat |
| title |
DBMUTE: density-based majority under-sampling technique |
| title_short |
DBMUTE: density-based majority under-sampling technique |
| title_full |
DBMUTE: density-based majority under-sampling technique |
| title_fullStr |
DBMUTE: density-based majority under-sampling technique |
| title_full_unstemmed |
DBMUTE: density-based majority under-sampling technique |
| title_sort |
dbmute: density-based majority under-sampling technique |
| publishDate |
2018 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84970990065&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57100 |
| _version_ |
1681424815668330496 |