Worthiness Benchmark: A novel concept for analyzing binary classification evaluation metrics Article Swipe
Mohammad Shirdel
,
Mario Di Mauro
,
Antonio Liotta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ins.2024.120882
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ins.2024.120882
Binary classification deals with identifying whether elements belong to one of two possible categories. Various metrics exist to evaluate the performance of such classification systems. It is important to study and contrast these metrics to find the best one for assessing a particular system. Despite extensive research in this field, a particular systematic comparison of these evaluation metrics remains an unaddressed area. The performance of a classifier is usually evaluated through the confusion matrix, a table including the count of accurate and inaccurate predictions for each category. To judge if one classifier is better than another, examining variations in the confusion matrix is necessary. However, no agreed-upon method exists for this analysis. This is crucial because different metrics may interpret and rate two confusion matrices differently. We introduce the Worthiness Benchmark (γ), a new concept useful to characterize the principles by which performance metrics rank classifiers. In particular, the Worthiness Benchmark is useful to assess how a metric evaluates the superiority among two classifiers by analyzing differences in their confusion matrices. Through this new concept, we are able to deal with the main challenge of selecting the best metric to evaluate a classifier. We then perform a γ-analysis on several binary classification metrics to outline the specific benchmarks these metrics follow when comparing different classifiers.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ins.2024.120882
- OA Status
- hybrid
- Cited By
- 6
- References
- 34
- Related Works
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- OpenAlex ID
- https://openalex.org/W4399566232
All OpenAlex metadata
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https://openalex.org/W4399566232Canonical identifier for this work in OpenAlex
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https://doi.org/10.1016/j.ins.2024.120882Digital Object Identifier
- Title
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Worthiness Benchmark: A novel concept for analyzing binary classification evaluation metricsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-06-12Full publication date if available
- Authors
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Mohammad Shirdel, Mario Di Mauro, Antonio LiottaList of authors in order
- Landing page
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https://doi.org/10.1016/j.ins.2024.120882Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.ins.2024.120882Direct OA link when available
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Benchmark (surveying), Computer science, Binary number, Binary classification, Data mining, Artificial intelligence, Machine learning, Mathematics, Support vector machine, Cartography, Arithmetic, GeographyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.outline | 204 |
| abstract_inverted_index.perform | 195 |
| abstract_inverted_index.remains | 58 |
| abstract_inverted_index.several | 199 |
| abstract_inverted_index.system. | 43 |
| abstract_inverted_index.through | 70 |
| abstract_inverted_index.usually | 68 |
| abstract_inverted_index.whether | 5 |
| abstract_inverted_index.However, | 104 |
| abstract_inverted_index.accurate | 80 |
| abstract_inverted_index.another, | 95 |
| abstract_inverted_index.concept, | 174 |
| abstract_inverted_index.contrast | 31 |
| abstract_inverted_index.elements | 6 |
| abstract_inverted_index.evaluate | 18, 190 |
| abstract_inverted_index.matrices | 124 |
| abstract_inverted_index.possible | 12 |
| abstract_inverted_index.research | 46 |
| abstract_inverted_index.specific | 206 |
| abstract_inverted_index.systems. | 24 |
| abstract_inverted_index.Benchmark | 130, 150 |
| abstract_inverted_index.analysis. | 111 |
| abstract_inverted_index.analyzing | 165 |
| abstract_inverted_index.assessing | 40 |
| abstract_inverted_index.category. | 86 |
| abstract_inverted_index.challenge | 183 |
| abstract_inverted_index.comparing | 212 |
| abstract_inverted_index.confusion | 72, 100, 123, 169 |
| abstract_inverted_index.different | 116, 213 |
| abstract_inverted_index.evaluated | 69 |
| abstract_inverted_index.evaluates | 158 |
| abstract_inverted_index.examining | 96 |
| abstract_inverted_index.extensive | 45 |
| abstract_inverted_index.important | 27 |
| abstract_inverted_index.including | 76 |
| abstract_inverted_index.interpret | 119 |
| abstract_inverted_index.introduce | 127 |
| abstract_inverted_index.matrices. | 170 |
| abstract_inverted_index.selecting | 185 |
| abstract_inverted_index.Worthiness | 129, 149 |
| abstract_inverted_index.benchmarks | 207 |
| abstract_inverted_index.classifier | 66, 91 |
| abstract_inverted_index.comparison | 53 |
| abstract_inverted_index.evaluation | 56 |
| abstract_inverted_index.inaccurate | 82 |
| abstract_inverted_index.necessary. | 103 |
| abstract_inverted_index.particular | 42, 51 |
| abstract_inverted_index.principles | 139 |
| abstract_inverted_index.systematic | 52 |
| abstract_inverted_index.variations | 97 |
| abstract_inverted_index.agreed-upon | 106 |
| abstract_inverted_index.categories. | 13 |
| abstract_inverted_index.classifier. | 192 |
| abstract_inverted_index.classifiers | 163 |
| abstract_inverted_index.differences | 166 |
| abstract_inverted_index.identifying | 4 |
| abstract_inverted_index.particular, | 147 |
| abstract_inverted_index.performance | 20, 63, 142 |
| abstract_inverted_index.predictions | 83 |
| abstract_inverted_index.superiority | 160 |
| abstract_inverted_index.unaddressed | 60 |
| abstract_inverted_index.γ-analysis | 197 |
| abstract_inverted_index.characterize | 137 |
| abstract_inverted_index.classifiers. | 145, 214 |
| abstract_inverted_index.differently. | 125 |
| abstract_inverted_index.classification | 1, 23, 201 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5004266537 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I131729948 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.91547746 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |