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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: Hossin, M., Sulaiman, M.N.
Format: Article
Language:English
Published: Academy & Industry Research Collaboration Center (AIRCC) 2015
Subjects:
TN Mining engineering. Metallurgy
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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spelling my.unimas.ir.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
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic TN Mining engineering. Metallurgy
spellingShingle TN Mining engineering. Metallurgy
Hossin, M.
Sulaiman, M.N.
A Review On Evaluation Metrics For Data Classification Evaluations
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.
format Article
author Hossin, M.
Sulaiman, M.N.
author_facet Hossin, M.
Sulaiman, M.N.
author_sort Hossin, M.
title A Review On Evaluation Metrics For Data Classification Evaluations
title_short 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_sort review on evaluation metrics for data classification evaluations
publisher Academy & Industry Research Collaboration Center (AIRCC)
publishDate 2015
url 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
_version_ 1745566030693924864
score 13.211869

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