Journal Article10.1007/S00500-007-0159-X
Applying genetic programming technique in classification trees
Chan-Sheng Kuo,Tzung-Pei Hong,Chuen-Lung Chen +2 more
- 07 Aug 2007
- Vol. 11, Iss: 12, pp 1165-1172
TL;DR: Two new genetic operators, elimination and merge, are designed in the proposed approach to remove redundancy and subsumption, thus producing more accurate and concise decision rules than that without using them.
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Abstract: Classification problems are often encountered in many applications. In the past, classification trees were often generated by decision-tree methods and commonly used to solve classification problems. In this paper, we have proposed an algorithm based on genetic programming to search for an appropriate classification tree according to some criteria. The classification tree obtained can be transferred into a rule set, which can then be fed into a knowledge base to support decision making and facilitate daily operations. Two new genetic operators, elimination and merge, are designed in the proposed approach to remove redundancy and subsumption, thus producing more accurate and concise decision rules than that without using them. Experimental results from the credit card data also show the feasibility of the proposed algorithm.
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Citations
A Survey on the Application of Genetic Programming to Classification
P.G. Espejo,Sebastián Ventura,Francisco Herrera +2 more
- 01 Mar 2010
TL;DR: This paper surveys existing literature about the application of genetic programming to classification, to show the different ways in which this evolutionary algorithm can help in the construction of accurate and reliable classifiers.
600
A Survey of Evolutionary Algorithms for Decision-Tree Induction
Rodrigo C. Barros,Márcio P. Basgalupp,A.C.P.L.F. de Carvalho,Alex A. Freitas +3 more
- 01 May 2012
TL;DR: This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction, which provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach.
Evolutionary Machine Learning: A Survey
TL;DR: In this article, the role of evolutionary machine learning (EC) algorithms in solving different ML challenges has been investigated, including feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods.
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Automatic Design of Decision-Tree Induction Algorithms
Rodrigo C. Barros,André C. P. L. F. de Carvalho,Alex A. Freitas +2 more
- 04 Feb 2015
TL;DR: This thesis proposes to automatically generate decision-tree induction algorithms based on the evolutionary algorithms paradigm, which improves solutions based on metaphors of biological processes and shows that HEAD-DT is prone to a special case of overfitting when it is executed under the second scenario of the general framework.
Induction of decision trees as classification models through metaheuristics
TL;DR: A review of the use of single-solution-based metaheuristics and swarm and evolutionary computation algorithms to build decision trees as classification models can be found in this paper, where the authors outline the decision tree-induction process components and detail the existing literature studies on metaheuristic-based approaches to building these classifiers.
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