Ensemble Learning Based Classification Algorithm Recommendation
Abstract
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.
Cite
@article{arxiv.2101.05993,
title = {Ensemble Learning Based Classification Algorithm Recommendation},
author = {Guangtao Wang and Qinbao Song and Xiaoyan Zhu},
journal= {arXiv preprint arXiv:2101.05993},
year = {2021}
}