A Review On Evaluation Metrics For Data Classification Evaluations
Evaluation metric plays a critical role in achieving the optimal classifier during the classification training. Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the optimal classifier. This paper systematically reviewed the related evaluation met...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Academy & Industry Research Collaboration Center (AIRCC)
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/13362/1/A%20review%20on%20evaluation%20metrics%20for%20data%20classification%20evaluations.pdf http://ir.unimas.my/id/eprint/13362/ http://search.proquest.com/openview/448ba332528146bc94cae9a25ffb31c8/1?pq-origsite=gscholar |
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| Summary: | Evaluation metric plays a critical role in achieving the optimal classifier during the classification training.
Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the
optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically
designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers
employ accuracy as a measure to discriminate the optimal solution during the classification training.
However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less
informativeness and bias to majority class data. This paper also briefly discusses other metrics that are
specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics
are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration
in constructing a new discriminator metric. |
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