A Comparative Analysis of Methods for Pruning Decision Trees

@article{Esposito1997ACA,
  title={A Comparative Analysis of Methods for Pruning Decision Trees},
  author={Floriana Esposito and Donato Malerba and Giovanni Semeraro},
  journal={IEEE Trans. Pattern Anal. Mach. Intell.},
  year={1997},
  volume={19},
  pages={476-491}
}
In this paper, we address the problem of retrospectively pruning decision trees induced from data, according to a topdown approach. This problem has received considerable attention in the areas of pattern recognition and machine learning, and many distinct methods have been proposed in literature. We make a comparative study of six well-known pruning methods with the aim of understanding their theoretical foundations, their computational complexity, and the strengths and weaknesses of their… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 31 references

On Estimating Probabilities in Tree Pruning

View 12 Excerpts
Highly Influenced

Expert Systems—Rule Induction With Statistical Data

J. Mingers
J. Operational Research Society, vol. 38, pp. 39-47, 1987. • 1987
View 10 Excerpts
Highly Influenced

Induction of Decision Trees

Machine Learning • 1986
View 13 Excerpts
Highly Influenced

Simplifying decision trees

Int. J. Hum.-Comput. Stud. • 1999
View 4 Excerpts
Highly Influenced

C4.5: Programs for Machine Learning

View 3 Excerpts
Highly Influenced

Digit Recognition Problem,

C. Schaffer, ”Deconstructing
Proc. Ninth Int’l Workshop on Machine Learning, • 1992
View 5 Excerpts
Highly Influenced

An Empirical Comparison of Pruning Methods for Decision Tree Induction

Machine Learning • 1989
View 8 Excerpts
Highly Influenced

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