A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data
The growing demand for complex machine learning models has increased the use of black-box models, such as random forests and artificial neural networks, posing significant challenges regarding explainability and interpretability. This manuscript addresses the critical problem of understanding and in...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10753588/ |
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| author | Rafael Rivera-Lopez Hector G. Ceballos |
| author_facet | Rafael Rivera-Lopez Hector G. Ceballos |
| author_sort | Rafael Rivera-Lopez |
| collection | DOAJ |
| description | The growing demand for complex machine learning models has increased the use of black-box models, such as random forests and artificial neural networks, posing significant challenges regarding explainability and interpretability. This manuscript addresses the critical problem of understanding and interpreting decisions from these opaque models, as a lack of interpretability can hinder their adoption in sensitive applications. To tackle this issue, we propose an evolutionary approach to induce univariate decision trees that accurately mimic the behavior of black-box models using tabular data. Our method employs two differential evolution algorithm variants, focusing on building univariate decision trees to enhance model explainability. Key contributions of this work include the development of a fitness function that balances accuracy with tree compactness to reduce overfitting and improve explanability. Additionally, we introduce a novel selection scheme that evaluates candidate solutions using synthetic instances, further enhancing the robustness against variance of the decision trees. Experimental results demonstrate that the proposed approach yields more precise and compact decision trees than traditional methods, significantly improving the explainability of complex machine learning models. |
| format | Article |
| id | doaj-art-e144ce46734a4c70b948e857ecf8bb30 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e144ce46734a4c70b948e857ecf8bb302025-08-20T02:50:48ZengIEEEIEEE Access2169-35362024-01-011216985016986810.1109/ACCESS.2024.349890710753588A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular DataRafael Rivera-Lopez0https://orcid.org/0000-0002-5254-4195Hector G. Ceballos1https://orcid.org/0000-0002-2460-3442Departamento de Sistemas y Computación, Tecnológico Nacional de México, Instituto Tecnológico de Veracruz, Veracruz, MexicoInstitute for the Future of Education, Tecnológico de Monterrey, Monterrey, MexicoThe growing demand for complex machine learning models has increased the use of black-box models, such as random forests and artificial neural networks, posing significant challenges regarding explainability and interpretability. This manuscript addresses the critical problem of understanding and interpreting decisions from these opaque models, as a lack of interpretability can hinder their adoption in sensitive applications. To tackle this issue, we propose an evolutionary approach to induce univariate decision trees that accurately mimic the behavior of black-box models using tabular data. Our method employs two differential evolution algorithm variants, focusing on building univariate decision trees to enhance model explainability. Key contributions of this work include the development of a fitness function that balances accuracy with tree compactness to reduce overfitting and improve explanability. Additionally, we introduce a novel selection scheme that evaluates candidate solutions using synthetic instances, further enhancing the robustness against variance of the decision trees. Experimental results demonstrate that the proposed approach yields more precise and compact decision trees than traditional methods, significantly improving the explainability of complex machine learning models.https://ieeexplore.ieee.org/document/10753588/Agnostic modelexplainable artificial intelligenceevolutionary computationdecision trees |
| spellingShingle | Rafael Rivera-Lopez Hector G. Ceballos A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data IEEE Access Agnostic model explainable artificial intelligence evolutionary computation decision trees |
| title | A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data |
| title_full | A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data |
| title_fullStr | A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data |
| title_full_unstemmed | A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data |
| title_short | A Differential-Evolution-Based Approach to Extract Univariate Decision Trees From Black-Box Models Using Tabular Data |
| title_sort | differential evolution based approach to extract univariate decision trees from black box models using tabular data |
| topic | Agnostic model explainable artificial intelligence evolutionary computation decision trees |
| url | https://ieeexplore.ieee.org/document/10753588/ |
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