Evolutionary response surfaces for classification: an interpretable model
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Response surfaces are powerful tools for both classification and regression because they are able to model many different phenomena and construct complex boundaries between classes. With very simple expressions, response surfaces are able to accurately solve difficult problems. Thus, the interpretability of the results is very interesting from the point of view of the expert, which is provided by a classification model from which useful information may be inferred.
However, response surfaces suffer from a major problem that limits their applicability. Even with a low degree and a moderate number of features, the number of terms in the surfaces is extremely large. Thus, standard learning algorithms find many problems to efficiently obtain the coefficients of the terms, and the risk of overfitting is high.
To overcome this problem we present evolutionary response surfaces for the classification of two-class problems. The use of a fitness function that combines accuracy and interpretability obtains accurate classifiers that are simple and interpretable by the expert. The results obtained for 20 problems from the UCI Machine Learning Repository are comparable with well-known classification algorithms with a more interpretable polynomial function.
KeywordsClassification Genetic algorithms Response surfaces
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