An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees
This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome...
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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/29/6/103 |
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| author | Rafael Rivera-López Efrén Mezura-Montes Juana Canul-Reich Marco-Antonio Cruz-Chávez |
| author_facet | Rafael Rivera-López Efrén Mezura-Montes Juana Canul-Reich Marco-Antonio Cruz-Chávez |
| author_sort | Rafael Rivera-López |
| collection | DOAJ |
| description | This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome these limitations, other strategies, such as those based on evolutionary computation, have been proposed in the literature. In particular, we evaluate the use of self-adaptive differential evolution variants to evolve a population of oblique decision trees encoded as real-valued vectors. Our proposal includes (1) an alternative initialization strategy that reduces redundant nodes and (2) a fitness function that penalizes excessive leaf nodes, promoting smaller and more accurate decision trees. We perform a comparative performance analysis of these differential evolution variants, showing that while they exhibit similar statistical behavior, the Single-Objective real-parameter optimization (jSO) method produces the most accurate oblique decision trees and is second best in compactness. The findings highlight the potential of self-adaptive differential evolution algorithms to improve the effectiveness of oblique decision trees in machine learning applications. |
| format | Article |
| id | doaj-art-f29650f238914cba8b661a04cf5e9ecc |
| institution | Kabale University |
| issn | 1300-686X 2297-8747 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj-art-f29650f238914cba8b661a04cf5e9ecc2024-12-27T14:38:25ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472024-11-0129610310.3390/mca29060103An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision TreesRafael Rivera-López0Efrén Mezura-Montes1Juana Canul-Reich2Marco-Antonio Cruz-Chávez3Departamento de Sistemas y Computación, Tecnológico Nacional de México, Instituto Tecnológico de Veracruz, Veracruz 91897, MexicoInstituto de Investigaciones en Inteligencia Artificial, Universidad Veracruzana, Xalapa 91097, MexicoDivisión Académica de Ciencias y Tecnologías de la Información, Universidad Juárez Autónoma de Tabasco, Cunduacán 86690, MexicoCentro de Investigación en Ingeniería y Ciencias Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca 62209, MexicoThis study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome these limitations, other strategies, such as those based on evolutionary computation, have been proposed in the literature. In particular, we evaluate the use of self-adaptive differential evolution variants to evolve a population of oblique decision trees encoded as real-valued vectors. Our proposal includes (1) an alternative initialization strategy that reduces redundant nodes and (2) a fitness function that penalizes excessive leaf nodes, promoting smaller and more accurate decision trees. We perform a comparative performance analysis of these differential evolution variants, showing that while they exhibit similar statistical behavior, the Single-Objective real-parameter optimization (jSO) method produces the most accurate oblique decision trees and is second best in compactness. The findings highlight the potential of self-adaptive differential evolution algorithms to improve the effectiveness of oblique decision trees in machine learning applications.https://www.mdpi.com/2297-8747/29/6/103machine learningevolutionary computationexplainable artificial intelligence |
| spellingShingle | Rafael Rivera-López Efrén Mezura-Montes Juana Canul-Reich Marco-Antonio Cruz-Chávez An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees Mathematical and Computational Applications machine learning evolutionary computation explainable artificial intelligence |
| title | An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees |
| title_full | An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees |
| title_fullStr | An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees |
| title_full_unstemmed | An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees |
| title_short | An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees |
| title_sort | experimental comparison of self adaptive differential evolution algorithms to induce oblique decision trees |
| topic | machine learning evolutionary computation explainable artificial intelligence |
| url | https://www.mdpi.com/2297-8747/29/6/103 |
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