Sun, Q., & Pfahringer, B. (2014). Hierarchical meta-rules for scalable meta-learning. In D.-N. Pham & S.-B. Park (Eds.), Proceedings of the 13th Pacific Rim International Conference on Artificial intelligence (Vol. LNCS 8862, pp. 383–395). Gold Coast, Australia: Springer Verlag. http://doi.org/10.1007/978-3-319-13560-1
Permanent Research Commons link: https://hdl.handle.net/10289/9333
The Pairwise Meta-Rules (PMR) method proposed in  has been shown to improve the predictive performances of several metalearning algorithms for the algorithm ranking problem. Given m target objects (e.g., algorithms), the training complexity of the PMR method with respect to m is quadratic: (formula presented). This is usually not a problem when m is moderate, such as when ranking 20 different learning algorithms. However, for problems with a much larger m, such as the meta-learning-based parameter ranking problem, where m can be 100+, the PMR method is less efficient. In this paper, we propose a novel method named Hierarchical Meta-Rules (HMR), which is based on the theory of orthogonal contrasts. The proposed HMR method has a linear training complexity with respect to m, providing a way of dealing with a large number of objects that the PMR method cannot handle efficiently. Our experimental results demonstrate the benefit of the new method in the context of meta-learning.