Software quality modeling: The impact of class noise on the random forest classifier

@article{Folleco2008SoftwareQM,
  title={Software quality modeling: The impact of class noise on the random forest classifier},
  author={Andres Folleco and Taghi M. Khoshgoftaar and Jason Van Hulse and Lofton A. Bullard},
  journal={2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)},
  year={2008},
  pages={3853-3859}
}
This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was utilized for this study because of its strong performance relative to well-known and commonly-used classifiers such as C4.5 and Naive Bayes. Further, relatively little prior… CONTINUE READING

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