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dc.contributor.authorSun, Quan
dc.contributor.authorPfahringer, Bernhard
dc.contributor.editorPham, D.-N.
dc.contributor.editorPark, S.-B.
dc.coverage.spatialGold Coast, Australia
dc.date.accessioned2015-05-22T03:14:59Z
dc.date.available2014
dc.date.available2015-05-22T03:14:59Z
dc.date.issued2014
dc.identifier.citationSun, 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/10.1007/978-3-319-13560-1en
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/10289/9333
dc.description.abstractThe Pairwise Meta-Rules (PMR) method proposed in [18] 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.
dc.format.extent383 - 395
dc.format.mimetypeapplication/pdf
dc.publisherSpringer Verlag
dc.sourcePRICAI 2014
dc.subjectMachine learning
dc.titleHierarchical meta-rules for scalable meta-learning
dc.typeConference Contribution
dc.identifier.doi10.1007/978-3-319-13560-1
dc.relation.isPartOfProceedings of the 13th Pacific Rim International Conference on Artificial intelligence
pubs.begin-page383
pubs.elements-id117964
pubs.end-page395
pubs.finish-date2014-12-05
pubs.start-date2014-12-01
pubs.volumeLNCS 8862
dc.identifier.eissn1611-3349


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