MUTE: Majority under-sampling technique
An application which operates on an imbalanced dataset loses its classification performance on a minority class, which is rare and important. There are a number of over-sampling techniques, which insert minority instances into a dataset, to adjust the class distribution. Unfortunately, these instanc...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Online Access: | http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84860630816&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38626 |
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| Institution: | Chiang Mai University |
| Summary: | An application which operates on an imbalanced dataset loses its classification performance on a minority class, which is rare and important. There are a number of over-sampling techniques, which insert minority instances into a dataset, to adjust the class distribution. Unfortunately, these instances highly affect the computation of generating a classifier. In this paper, a new simple and effective under-sampling called MUTE is proposed. Its strategy is to get rid of noise majority instances which over-lap with minority instances. The removal majority instances are considered based on their safe levels relying on the Safe-Level-SMOTE concept. MUTE not only reduces the classifier construction time because of a downsizing dataset but also improves the prediction rate on a minority class. The experimental results show that MUTE improves F-measure by comparing to SMOTE techniques. © 2011 IEEE. |
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