- Published 2014 in Journal of Machine Learning Research

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearestneighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

Citations per Year

Semantic Scholar estimates that this publication has **1,009** citations based on the available data.

See our **FAQ** for additional information.

Showing 1-10 of 265 extracted citations

Highly Influenced

20 Excerpts

Highly Influenced

6 Excerpts

Highly Influenced

7 Excerpts

Highly Influenced

9 Excerpts

Highly Influenced

10 Excerpts

Highly Influenced

6 Excerpts

Highly Influenced

4 Excerpts

Highly Influenced

9 Excerpts

Highly Influenced

4 Excerpts

Highly Influenced

5 Excerpts

Showing 1-10 of 77 references

Highly Influential

4 Excerpts

Highly Influential

10 Excerpts

Highly Influential

11 Excerpts

Highly Influential

7 Excerpts

Highly Influential

6 Excerpts

Highly Influential

7 Excerpts

Highly Influential

4 Excerpts

Highly Influential

3 Excerpts

Highly Influential

2 Excerpts

Highly Influential

2 Excerpts

@article{Delgado2014DoWN,
title={Do we need hundreds of classifiers to solve real world classification problems?},
author={Manuel Fern{\'a}ndez Delgado and Eva Cernadas and Sen{\'e}n Barro and Dinani Gomes Amorim},
journal={Journal of Machine Learning Research},
year={2014},
volume={15},
pages={3133-3181}
}