Inductive Genetic Programming with Decision Trees

Nikolaev, Nikolay; Slavov, V.

doi:10.1007/3-540-62858-4_83

Cited by 20 publications

(22 citation statements)

References 4 publications

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“…Similarly, Reynolds and Al-Shehri [52] propose a fitness function based on accuracy, number of nodes, number of attributes used and homogeneity of each partition. Nikolaev and Slavov [82] offer a variation of Quinlan's pruning strategy presented in [83] as a stochastic fitness function, and perform a detailed analysis of the fitness landscape structure.…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

“…Another popular choice in EAs for decision tree induction is the roulette wheel selection.Works that implement this strategy for evolving decision trees are [35]- [37], [42]- [44], [48], [51], [55], [60]- [63], [73], [82], [86], [87].…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

“…This crossover selects nodes in two individuals and exchanges the entire subtrees corresponding to each selected node, generating two offspring. This operator is used in [28], [35]- [45], [51], [55]- [58], [61]- [67], [69], [73], [77]- [82], [85]- [88]. Two small variations of this strategy are found in the literature.…”

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Barros

Basgalupp

Carvalho

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

298

139

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Abstract-This paper presents a survey of evolutionary algorithms designed for decision tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divideand-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision tree classifiers. The paper original contributions are the following. First, it provides an upto-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy which addresses works that evolve decision trees and works that design decision tree components using evolutionary algorithms. Finally, a number of references is provided that describe applications of evolutionary algorithms for decision tree induction in different domains. The paper ends by addressing some important issues and open questions that can be subject of future research.

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Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

Section: A Axis-parallel Decision Treesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Barros

Basgalupp

Carvalho

et al. 2012

IEEE Trans. Syst., Man, Cybern. C

298

139

View full text Add to dashboard Cite

show abstract

“…The GP approach uses the evolutionary computation paradigm to evolve computer programs, according to a user-defined fitness function. When dealing with classification problems, GP-based techniques exhibited very interesting performance [12]. In this context, the decision tree [14] data structure is typically adopted since it allows to effectively arrange in a tree-structured plans the set of attributes chosen for pattern representation.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach for Combining Ensembles of GP Classifiers

Stefano

Fontanella

Folino

2011

Multiple Classifier Systems

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Abstract. Recently, ensemble techniques have also attracted the attention of Genetic Programing (GP) researchers. The goal is to further improve GP classification performances. Among the ensemble techniques, also bagging and boosting have been taken into account. These techniques improve classification accuracy by combining the responses of different classifiers by using a majority vote rule. However, it is really hard to ensure that classifiers in the ensemble be appropriately diverse, so as to avoid correlated errors. Our approach tries to cope with this problem, designing a framework for effectively combine GP-based ensemble by means of a Bayesian Network. The proposed system uses two different approaches. The first one applies a boosting technique to a GP-based classification algorithm in order to generate an effective decision trees ensemble. The second module uses a Bayesian network for combining the responses provided by such ensemble and select the most appropriate decision trees. The Bayesian network is learned by means of a specifically devised Evolutionary algorithm. Preliminary experimental results confirmed the effectiveness of the proposed approach.

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“…In the framework of univariate trees, most of the research was concentrated on global induction (e.g. [11,18,19,14]), whereas for linear trees mainly top-down methods were developed, where only splitting hyper-planes in internal nodes were evolutionary searched (e.g. [5,4,12]).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Learning of Linear Trees with Embedded Feature Selection

Krȩtowski

Grzes

2006

Artificial Intelligence and Soft Computing – ICAISC 2006

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Abstract. In the paper a new evolutionary algorithm for global induction of linear trees is presented. The learning process consists of searching for both a decision tree structure and hyper-plane weights in all nonterminal nodes. Specialized genetic operators are developed and applied according to the node quality and location. Feature selection aimed at simplification of the splitting hyper-planes is embedded into the algorithm and results in elimination of noisy and redundant features. The proposed approach is verified on both artificial and real-life data and the obtained results are promising.

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Inductive Genetic Programming with Decision Trees

Cited by 20 publications

References 4 publications

A Survey of Evolutionary Algorithms for Decision-Tree Induction

A Survey of Evolutionary Algorithms for Decision-Tree Induction

A Bayesian Approach for Combining Ensembles of GP Classifiers

Evolutionary Learning of Linear Trees with Embedded Feature Selection

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