Ensemble Learning Based Classification Algorithm Recommendation
This addresses the challenge of algorithm selection in data mining, but it is incremental as it builds on existing ensemble and meta-feature approaches.
The paper tackles the problem of recommending classification algorithms by proposing an ensemble learning-based method that leverages multiple meta-features, showing effectiveness through experiments on 1090 benchmark problems with 13 algorithms.
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.