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A GENETIC PROGRAMMING-BASED LEARNING ALGORITHMS FOR PRUNING COST-SENSITIVE CLASSIFIERS

ZAHRA NIKDEL

Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

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and

HAMID BEIGY

Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

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https://doi.org/10.1142/S1469026812500113Cited by:4 (Source: Crossref)

Abstract

In this paper, we introduce a new hybrid learning algorithm, called DTGP, to construct cost-sensitive classifiers. This algorithm uses a decision tree as its basic classifier and the constructed decision tree will be pruned by a genetic programming algorithm using a fitness function that is sensitive to misclassification costs. The proposed learning algorithm has been examined through six cost-sensitive problems. The experimental results show that the proposed learning algorithm outperforms in comparison to some other known learning algorithms like C4.5 or naïve Bayesian.

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