Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles
This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, f...
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/35 |
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| author | Evans Teiko Tetteh Beata Zielosko |
| author_facet | Evans Teiko Tetteh Beata Zielosko |
| author_sort | Evans Teiko Tetteh |
| collection | DOAJ |
| description | This study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules that are true across multiple decision trees. Experiments are performed, taking into account knowledge representation and discovery perspectives. They show that, as the value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>≤</mo><mi>α</mi><mo><</mo><mn>1</mn></mrow></semantics></math></inline-formula>, increases, shorter rules are obtained, and also it is possible to improve the classification accuracy of rule-based models. |
| format | Article |
| id | doaj-art-c1fe016f2ced42ee83144ede414c5fec |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-c1fe016f2ced42ee83144ede414c5fec2025-01-24T13:31:45ZengMDPI AGEntropy1099-43002025-01-012713510.3390/e27010035Greedy Algorithm for Deriving Decision Rules from Decision Tree EnsemblesEvans Teiko Tetteh0Beata Zielosko1Institute of Computer Science, University of Silesia in Katowice, Bȩdzińska 39, 41-200 Sosnowiec, PolandInstitute of Computer Science, University of Silesia in Katowice, Bȩdzińska 39, 41-200 Sosnowiec, PolandThis study introduces a greedy algorithm for deriving decision rules from decision tree ensembles, targeting enhanced interpretability and generalization in distributed data environments. Decision rules, known for their transparency, provide an accessible method for knowledge extraction from data, facilitating decision-making processes across diverse fields. Traditional decision tree algorithms, such as CART and ID3, are employed to induce decision trees from bootstrapped datasets, which represent distributed data sources. Subsequently, a greedy algorithm is applied to derive decision rules that are true across multiple decision trees. Experiments are performed, taking into account knowledge representation and discovery perspectives. They show that, as the value of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>≤</mo><mi>α</mi><mo><</mo><mn>1</mn></mrow></semantics></math></inline-formula>, increases, shorter rules are obtained, and also it is possible to improve the classification accuracy of rule-based models.https://www.mdpi.com/1099-4300/27/1/35decision treesdecision rulesgreedy algorithmlengthensemble |
| spellingShingle | Evans Teiko Tetteh Beata Zielosko Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles Entropy decision trees decision rules greedy algorithm length ensemble |
| title | Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles |
| title_full | Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles |
| title_fullStr | Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles |
| title_full_unstemmed | Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles |
| title_short | Greedy Algorithm for Deriving Decision Rules from Decision Tree Ensembles |
| title_sort | greedy algorithm for deriving decision rules from decision tree ensembles |
| topic | decision trees decision rules greedy algorithm length ensemble |
| url | https://www.mdpi.com/1099-4300/27/1/35 |
| work_keys_str_mv | AT evansteikotetteh greedyalgorithmforderivingdecisionrulesfromdecisiontreeensembles AT beatazielosko greedyalgorithmforderivingdecisionrulesfromdecisiontreeensembles |