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This work presents a content-based recommender system for machine learningclassifier algorithms. Given a new data set, a recommendation of whatclassifier is likely to perform best is made based on classifier performanceover similar known data sets. This similarity is measured according to a dataset characterization that includes several state-of-the-art metrics taking intoaccount physical structure, statis- tics, and information theory. A noveltywith respect to prior work is the use of a robust approach based on permutationtests to directly assess whether a given learning algorithm is able to exploitthe attributes in a data set to predict class labels, and compare it to themore commonly used F-score metric for evalu- ating classifier performance. Toevaluate our approach, we have conducted an extensive experimentation including8 of the main machine learning classification methods with varyingconfigurations and 65 bi- nary data sets, leading to over 2331 experiments. Ourresults show that using the information from the permutation test clearlyimproves the quality of the recommendations.

Classifier Selection with Permutation Tests