Chiang Mai Journal of Science

Safe Level Graph for Majority Under-sampling Techniques

Paper Type	Contributed Paper
Title	Safe Level Graph for Majority Under-sampling Techniques
Author	Chumphol Bunkhumpornpat* [a] and Krung Sinapiromsaran [b]
Email	chumphol@chiangmai.ac.th
Abstract:  In classification tasks, imbalance data causes the inadequate predictive performance of a tiny minority class because the decision boundary determined by trivial classifiers tends to be biased toward a huge majority class. For handling the class imbalance problem, over- and under-sampling are applied at the data level. Over-sampling duplicates or synthesizes instances into a minority class. Although redundant instances do not harm correct classifications, they increase classification costs. Additionally, while synthetic instances expand the learning region, they are not actual instances. Under-sampling removes instances from a majority class to remedy the overlapping problem. Consequently, a downsized dataset can speed up a classification algorithm. This study investigates the behavior of several under-sampling techniques, while cleansing distinct majority class regions. We also propose a safe level graph to justify an appropriate parameter of our prior work, MUTE. The experiment shows that our decision from a safe level graph can improve the F-measure of RIPPER when evaluating minority classes.
Start & End Page	1419 - 1428
Received Date	2012-08-30
Revised Date
Accepted Date	2013-08-12
Full Text	Download
Keyword	classification, class imbalance, under-sampling, MUTE, safe level graph, RIPPER
Volume	Vol.41 No.5/2 (OCTOBER 2014)
DOI
SDGs
View:479 Download:153