A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method

Abstract

As more and more classification algorithms continue to be developed, recommending appropriate algorithms to a given classification problem is increasingly important. This article first distinguishes the algorithm recommendation methods by two dimensions: (1) meta-features, which are a set of measures used to characterize the learning problems, and (2) meta-target, which represents the relative performance of the classification algorithms on the learning problem. In contrast to the existing algorithm recommendation methods whose meta-target is usually in the form of either the ranking of candidate algorithms or a single algorithm, this article proposes a new and natural multilabel form to describe the meta-target. This is due to the fact that there would be multiple algorithms being appropriate for a given problem in practice. Furthermore, a novel multilabel learning-based generic algorithm recommendation method is proposed, which views the algorithm recommendation as a multilabel learning problem and solves the problem by the mature multilabel learning algorithms. To evaluate the proposed multilabel learning-based recommendation method, extensive experiments with 13 well-known classification algorithms, two kinds of meta-targets such as algorithm ranking and single algorithm, and five different kinds of meta-features are conducted on 1,090 benchmark learning problems. The results show the effectiveness of our proposed multilabel learning-based recommendation method.