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...
| 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/ http://ir.unimas.my/id/eprint/13362/1/A%20review%20on%20evaluation%20metrics%20for%20data%20classification%20evaluations.pdf |
| _version_ | 1848837390027718656 |
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| author | Hossin, M. Sulaiman, M.N. |
| author_facet | Hossin, M. Sulaiman, M.N. |
| author_sort | Hossin, M. |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T06:38:54Z |
| format | Article |
| id | unimas-13362 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:38:54Z |
| publishDate | 2015 |
| publisher | Academy & Industry Research Collaboration Center (AIRCC) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-133622022-09-29T06:16:57Z http://ir.unimas.my/id/eprint/13362/ A Review On Evaluation Metrics For Data Classification Evaluations Hossin, M. Sulaiman, M.N. TN Mining engineering. Metallurgy 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. Academy & Industry Research Collaboration Center (AIRCC) 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/13362/1/A%20review%20on%20evaluation%20metrics%20for%20data%20classification%20evaluations.pdf Hossin, M. and Sulaiman, M.N. (2015) A Review On Evaluation Metrics For Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5 (2). pp. 1-11. ISSN 2230 - 9608 http://search.proquest.com/openview/448ba332528146bc94cae9a25ffb31c8/1?pq-origsite=gscholar DOI : 10.5121/ijdkp.2015.5201 |
| spellingShingle | TN Mining engineering. Metallurgy Hossin, M. Sulaiman, M.N. A Review On Evaluation Metrics For Data Classification Evaluations |
| title | A Review On Evaluation Metrics For Data Classification Evaluations |
| title_full | A Review On Evaluation Metrics For Data Classification Evaluations |
| title_fullStr | A Review On Evaluation Metrics For Data Classification Evaluations |
| title_full_unstemmed | A Review On Evaluation Metrics For Data Classification Evaluations |
| title_short | A Review On Evaluation Metrics For Data Classification Evaluations |
| title_sort | review on evaluation metrics for data classification evaluations |
| topic | TN Mining engineering. Metallurgy |
| url | http://ir.unimas.my/id/eprint/13362/ http://ir.unimas.my/id/eprint/13362/ http://ir.unimas.my/id/eprint/13362/ http://ir.unimas.my/id/eprint/13362/1/A%20review%20on%20evaluation%20metrics%20for%20data%20classification%20evaluations.pdf |