A CART-based genetic algorithm for constructing higher accuracy decision trees

Name: A CART-based genetic algorithm for constructing higher accuracy decision trees
Author: Ersoy, Elif, Albey, Erinç, Kayış, Enis

İsim	A CART-based genetic algorithm for constructing higher accuracy decision trees
Yazar	Ersoy, Elif, Albey, Erinç, Kayış, Enis
Basım Tarihi:	2020
Basım Yeri	- SciTePress
Konu	Decision tree, Heuristic, Genetic algorithm, Metaheuristic
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-989758440-4
Kayıt Numarası	d6cd0189-056a-480f-9275-5300a53d1904
Lokasyon	Industrial Engineering
Tarih	2020
Örnek Metin	Decision trees are among the most popular classification methods due to ease of implementation and simple interpretation. In traditional methods like CART (classification and regression tree), ID4, C4.5; trees are constructed by myopic, greedy top-down induction strategy. In this strategy, the possible impact of future splits in the tree is not considered while determining each split in the tree. Therefore, the generated tree cannot be the optimal solution for the classification problem. In this paper, to improve the accuracy of the decision trees, we propose a genetic algorithm with a genuine chromosome structure. We also address the selection of the initial population by considering a blend of randomly generated solutions and solutions from traditional, greedy tree generation algorithms which is constructed for reduced problem instances. The performance of the proposed genetic algorithm is tested using different datasets, varying bounds on the depth of the resulting trees and using different initial population blends within the mentioned varieties. Results reveal that the performance of the proposed genetic algorithm is superior to that of CART in almost all datasets used in the analysis.
Editör	Hammoudi, S., Quix, C., Bernardino, J.

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A CART-based genetic algorithm for constructing higher accuracy decision trees

Yazar Ersoy, Elif, Albey, Erinç, Kayış, Enis

Basım Tarihi 2020

Basım Yeri - SciTePress

Konu Decision tree, Heuristic, Genetic algorithm, Metaheuristic

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-989758440-4

Kayıt Numarası d6cd0189-056a-480f-9275-5300a53d1904

Lokasyon Industrial Engineering

Tarih 2020

Örnek Metin Decision trees are among the most popular classification methods due to ease of implementation and simple interpretation. In traditional methods like CART (classification and regression tree), ID4, C4.5; trees are constructed by myopic, greedy top-down induction strategy. In this strategy, the possible impact of future splits in the tree is not considered while determining each split in the tree. Therefore, the generated tree cannot be the optimal solution for the classification problem. In this paper, to improve the accuracy of the decision trees, we propose a genetic algorithm with a genuine chromosome structure. We also address the selection of the initial population by considering a blend of randomly generated solutions and solutions from traditional, greedy tree generation algorithms which is constructed for reduced problem instances. The performance of the proposed genetic algorithm is tested using different datasets, varying bounds on the depth of the resulting trees and using different initial population blends within the mentioned varieties. Results reveal that the performance of the proposed genetic algorithm is superior to that of CART in almost all datasets used in the analysis.

Editör Hammoudi, S., Quix, C., Bernardino, J.