mjuez / fern-classifiers

🌿🧠 Fern based classifiers for scikit-learn.

Fern-based classifiers

In a nutshell, a fern is a decision tree where all nodes of each depth level use the same feature and split criteria. Although it is simpler than a tree, when used as an ensemble of ferns, it turns fast and powerful!

Random Ferns were first proposed in [1] for image keypoint recognition tasks. Later, in [2] a general purpose machine learning method was developed in R, and is the base line of this study.

Our implementations follow the python scikit-learn API.

Authors

This implementation was developed and maintained by Mario Juez-Gil from ADMIRABLE research group of the University of Burgos, with the help and useful advice from Álvar Arnaiz-González, Juan J. Rodriguez, and Ludmila Kuncheva. The work was supervised by César García-Osorio and Carlos López-Nozal.

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Usage

Cython was used for speeding the algorithms. Because of that, the code must be compiled as follows:

python setup.py build_ext --inplace

Once compiled, it can be used like any scikit-learn classifier. A basic example could be:

example.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from fc.ferns import FernClassifier

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target,
                                                    test_size=0.5)
fern = FernClassifier(depth=5, random_state=1)
fern.fit(X_train, y_train)
y_pred = fern.predict(X_test)
print(confusion_matrix(y_test, y_pred))

output:

[[24  1  0]
 [ 3 19  1]
 [ 0  7 20]]

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References

[1] M. Ozuysal, M. Calonder, V. Lepetit, and P. Fua, “Fast Keypoint Recognition Using Random Ferns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 3, pp. 448–461, Mar. 2010. DOI: 10.1109/TPAMI.2009.23

[2] M. B. Kursa, “rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning,” J. Stat. Softw., vol. 61, no. 10, pp. 1–13, Nov. 2014. DOI: 10.18637/jss.v061.i10

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License

Licensed under the GNU GPLv3, please see the LICENSE file for more details.

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Acknowledgements

This work was partially supported by the Consejería de Educación of the Junta de Castilla y León and by the European Social Fund with the EDU/1100/2017 pre-doctoral grants; by the project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía Competitividad of the Spanish Government and the project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León both cofinanced from European Union FEDER funds.