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.

中文翻译：

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基于集成学习的分类算法推荐

将适当的算法推荐给分类问题是数据挖掘领域中最具挑战性的问题之一。现有的算法推荐模型通常由单个学习者仅基于一种元特征来构建。考虑到i）整体学习者通常表现出更好的表现，并且ii）不同种类的元特征独立地描述了不同观点中的分类问题，并且具有不同元特征集的模型将相互补充并适用于整体。提出了一种基于整体学习的算法推荐方法。为了评估建议的推荐方法，针对1090个基准分类问题，使用13种著名的候选分类算法和5种不同的元特征进行了广泛的实验。结果表明，提出的基于集成学习的推荐方法是有效的。