Inductive genetic programming with decision trees

Nikolaev, Nikolay; Slavov, V.

doi:10.1016/s1088-467x(98)00005-5

Cited by 21 publications

(2 citation statements)

References 13 publications

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“…Koza introduced the idea of using GP to induce a decision tree classifier, which was represented by a LISP S-expression [19]. Since then, several investigations have employed GP to develop decision trees [4], [23], [25]. The basic ideas involved in using GP for building a decision tree are as follows.…”

Section: A Quick Review Of Evolutionary Algorithms For Classificationmentioning

confidence: 99%

Multi-objective Classification Rule Mining Using Gene Expression Programming

Dehuri

Cho

2008

2008 Third International Conference on Convergence and Hybrid Information Technology

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In this paper, the classification rule-mining problem is considered as a multi-objective problem rather than a uni-objective one. Metrics like predictive accuracy and comprehensibility, used for evaluating a rule can be thought of as different criteria of this problem. Predictive accuracy measures the accuracy of the rules extracted from the dataset where as, comprehensibility is measured by the number of attributes involved in the rule and tries to quantify the understandability of the rule. Using these measures as the objectives of rule mining problem, this paper uses gene expression programming to extract some useful and understandable rule. The discovered rule/knowledge is expressed in the form of IF-THEN high-level statement. Gene expression programming recently been introduced as one of the components of evolutionary algorithms and its attributes like simple linear representation and easy to implement, motivate us to use for mining classification rule with multiple objectives. It is often criticized when applied to classification rule mining with multiple objectives, because of the amount of computational resources it requires. However, we believe that it has a lot of potential to perform global search by exploring a large space. The rule antecedent part may contain different combinations of predictor attributes while the consequent part contains only the goal attribute. The searching process is guided by a fitness function considering both predictive accuracy and comprehensibility. Experiments with several benchmark datasets have generated rules for each class with acceptable predictive accuracy and comprehensibility.

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Section: A Quick Review Of Evolutionary Algorithms For Classificationmentioning

confidence: 99%

Multi-objective Classification Rule Mining Using Gene Expression Programming

Dehuri

Cho

2008

2008 Third International Conference on Convergence and Hybrid Information Technology

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“…Turney (1995) applied genetic algorithms to search through the space of decision trees generated by top-down learners (focusing on cost-sensitive learning). Later on, Nikolaev and Slavov (1998) analyzed a global fitness landscape structure and its application on decision tree building, while Bot and Langdon (2000) used genetic programming to evolve linear classification trees. Subsequently, the GATree system (Papagelis and Kalles 2001) employed a fitness function to trade off tree size (as expressed by the number of leaves) against accuracy in its quest for a good tree.…”

Section: Introductionmentioning

confidence: 99%

Lossless fitness inheritance in genetic algorithms for decision trees

Kalles

Papagelis

2009

Soft Comput

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When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is sufficient for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speedup on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide-and-conquer nature of decision trees. The theoretical results are supported by experimental evidence.

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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 21 publications

References 13 publications

Multi-objective Classification Rule Mining Using Gene Expression Programming

Multi-objective Classification Rule Mining Using Gene Expression Programming

Lossless fitness inheritance in genetic algorithms for decision trees

Evolutionary Learning of Linear Trees with Embedded Feature Selection

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