A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria

Daren Ler, Irena Koprinska, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For a given classification task, there are typically several learning algorithms available. The question then arises: which is the most appropriate algorithm to apply. Recently, we proposed a new algorithm for making such a selection based on landmarking - a meta-learning strategy that utilises meta-features that are measurements based on efficient learning algorithms. This algorithm, which creates a set of landmarkers that each utilise subsets of the algorithms being landmarked, was shown to be able to estimate accuracy well, even when employing a small fraction of the given algorithms. However, that version of the algorithm has exponential computational complexity for training. In this paper, we propose a hill-climbing version of the landmarker generation algorithm, which requires only polynomial training time complexity. Our experiments show that the landmarkers formed have similar results to the more complex version of the algorithm.

Original languageEnglish
Title of host publicationProceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence
EditorsI. Russell, Z. Markov
Pages418-423
Number of pages6
Publication statusPublished - 2005
Externally publishedYes
EventRecent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Clearwater Beach, FL
Duration: 15 May 200517 May 2005

Other

OtherRecent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005
CityClearwater Beach, FL
Period15/5/0517/5/05

Fingerprint

Set theory
Learning algorithms
Computational complexity
Polynomials
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ler, D., Koprinska, I., & Chawla, S. (2005). A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria. In I. Russell, & Z. Markov (Eds.), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence (pp. 418-423)

A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria. / Ler, Daren; Koprinska, Irena; Chawla, Sanjay.

Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. ed. / I. Russell; Z. Markov. 2005. p. 418-423.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ler, D, Koprinska, I & Chawla, S 2005, A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria. in I Russell & Z Markov (eds), Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. pp. 418-423, Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005, Clearwater Beach, FL, 15/5/05.
Ler D, Koprinska I, Chawla S. A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria. In Russell I, Markov Z, editors, Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. 2005. p. 418-423
Ler, Daren ; Koprinska, Irena ; Chawla, Sanjay. / A hill-climbing landmarker generation algorithm based on efficiency and correlativity criteria. Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence. editor / I. Russell ; Z. Markov. 2005. pp. 418-423
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