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A genetic programming-based learning algorithms for pruning cost-sensitive classifiers

Nikdel, Z ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1142/S1469026812500113
  3. Publisher: 2012
  4. Abstract:
  5. 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
  6. Keywords:
  7. Machine learning ; Cost-sensitive ; Cost-sensitive classification ; Fitness functions ; Hybrid learning algorithm ; Misclassification costs ; Programming algorithms ; Costs ; Decision trees ; Evolutionary algorithms ; Genetic programming ; Learning systems ; Learning algorithms
  8. Source: International Journal of Computational Intelligence and Applications ; Volume 11, Issue 2 , June , 2012 ; 14690268 (ISSN)
  9. URL: http://www.worldscientific.com/doi/abs/10.1142/S1469026812500113