A landmarker selection algorithm based on correlation and efficiency criteria

Daren Ler, Irena Koprinska, Sanjay Chawla

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

1 Citation (Scopus)

Abstract

Landmarking is a recent and promising meta-learning strategy, which defines meta-features that are themselves efficient learning algorithms. However, the choice of landmarkers is often made in an ad hoc manner. In this paper, we propose a new perspective and set of criteria for landmarkers. Based on the new criteria, we propose a landmarker generation algorithm, which generates a set of landmarkers that are each subsets of the algorithms being landmarked. Our experiments show that the landmarkers formed, when used with linear regression are able to estimate the accuracy of a set of candidate algorithms well, while only utilising a small fraction of the computational cost required to evaluate those candidate algorithms via ten-fold cross-validation.

Original languageEnglish
Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
EditorsG.I. Webb, X. Yu
Pages296-306
Number of pages11
Volume3339
Publication statusPublished - 2004
Externally publishedYes
Event17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia
Duration: 4 Dec 20046 Dec 2004

Other

Other17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence
CountryAustralia
CityCairns
Period4/12/046/12/04

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ASJC Scopus subject areas

  • Hardware and Architecture

Cite this

Ler, D., Koprinska, I., & Chawla, S. (2004). A landmarker selection algorithm based on correlation and efficiency criteria. In G. I. Webb, & X. Yu (Eds.), Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3339, pp. 296-306)