Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniq...
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MDPI AG
2024-11-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/17/11/518 |
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| author | Pablo Neirz Hector Allende Carolina Saavedra |
| author_facet | Pablo Neirz Hector Allende Carolina Saavedra |
| author_sort | Pablo Neirz |
| collection | DOAJ |
| description | This study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>h</mi><msub><mi>i</mi><mi>k</mi></msub></mrow></semantics></math></inline-formula>. This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others. |
| format | Article |
| id | doaj-art-2662aec2941d477cac3e5eee8af0744a |
| institution | OA Journals |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-2662aec2941d477cac3e5eee8af0744a2025-08-20T02:26:44ZengMDPI AGAlgorithms1999-48932024-11-01171151810.3390/a17110518Attribute Relevance Score: A Novel Measure for Identifying Attribute ImportancePablo Neirz0Hector Allende1Carolina Saavedra2Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 1680, ChileDepartamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 1680, ChileDepartamento de Informática, Universidad Técnica Federico Santa María, Valparaíso 1680, ChileThis study introduces a novel measure for evaluating attribute relevance, specifically designed to accurately identify attributes that are intrinsically related to a phenomenon, while being sensitive to the asymmetry of those relationships and noise conditions. Traditional variable selection techniques, such as filter and wrapper methods, often fall short in capturing these complexities. Our methodology, grounded in decision trees but extendable to other machine learning models, was rigorously evaluated across various data scenarios. The results demonstrate that our measure effectively distinguishes relevant from irrelevant attributes and highlights how relevance is influenced by noise, providing a more nuanced understanding compared to established methods such as Pearson, Spearman, Kendall, MIC, MAS, MEV, GMIC, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>h</mi><msub><mi>i</mi><mi>k</mi></msub></mrow></semantics></math></inline-formula>. This research underscores the importance of phenomenon-centric explainability, reproducibility, and robust attribute relevance evaluation in the development of predictive models. By enhancing both the interpretability and contextual accuracy of models, our approach not only supports more informed decision making but also contributes to a deeper understanding of the underlying mechanisms in diverse application domains, such as biomedical research, financial modeling, astronomy, and others.https://www.mdpi.com/1999-4893/17/11/518feature importancefeature selectionfeature rankingdependency measuresmachine learning explainabilityall-relevant problem |
| spellingShingle | Pablo Neirz Hector Allende Carolina Saavedra Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance Algorithms feature importance feature selection feature ranking dependency measures machine learning explainability all-relevant problem |
| title | Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance |
| title_full | Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance |
| title_fullStr | Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance |
| title_full_unstemmed | Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance |
| title_short | Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance |
| title_sort | attribute relevance score a novel measure for identifying attribute importance |
| topic | feature importance feature selection feature ranking dependency measures machine learning explainability all-relevant problem |
| url | https://www.mdpi.com/1999-4893/17/11/518 |
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