  Recommending appropriate algorithms to a classification problem is one of the
most challenging issues in the field of data mining. The existing algorithm
recommendation models are generally constructed on only one kind of
meta-features by single learners. Considering that i) ensemble learners usually
show better performance and ii) different kinds of meta-features characterize
the classification problems in different viewpoints independently, and further
the models constructed with different sets of meta-features will be
complementary with each other and applicable for ensemble. This paper proposes
an ensemble learning-based algorithm recommendation method. To evaluate the
proposed recommendation method, extensive experiments with 13 well-known
candidate classification algorithms and five different kinds of meta-features
are conducted on 1090 benchmark classification problems. The results show the
effectiveness of the proposed ensemble learning based recommendation method.
