Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm

@article{Tripoliti2012AutomatedDO,
  title={Automated Diagnosis of Diseases Based on Classification: Dynamic Determination of the Number of Trees in Random Forests Algorithm},
  author={Evanthia E. Tripoliti and Dimitrios I. Fotiadis and George Manis},
  journal={IEEE Transactions on Information Technology in Biomedicine},
  year={2012},
  volume={16},
  pages={615-622}
}
The accurate diagnosis of diseases with high prevalence rate, such as Alzheimer, Parkinson, diabetes, breast cancer, and heart diseases, is one of the most important biomedical problems whose administration is imperative. In this paper, we present a new method for the automated diagnosis of diseases based on the improvement of random forests classification algorithm. More specifically, the dynamic determination of the optimum number of base classifiers composing the random forests is addressed… CONTINUE READING

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References

Publications referenced by this paper.
Showing 1-10 of 26 references

Improving Random Forests

View 6 Excerpts
Highly Influenced

A Comparison of Decision Tree Ensemble Creation Techniques

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2007
View 3 Excerpts
Highly Influenced

Dynamic construction of Random Forests: Evaluation using biomedical engineering problems

Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine • 2010

Dynamic Classifier Ensemble Selection Based on GMDH

2009 International Joint Conference on Computational Sciences and Optimization • 2009
View 3 Excerpts

On the selection of decision trees in Random Forests

2009 International Joint Conference on Neural Networks • 2009
View 2 Excerpts

A taxonomy of similarity mechanisms for case-based reasoning,

P. Cunningham
2008