Improved data set characterisation for meta-learning

Y Peng, PA Flach, C Soares, P Brazdil

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)


This paper presents new measures, based on the induced decision tree, to characterise datasets for meta-learning in order to select appropriate learning algorithms. The main idea is to capture the characteristics of dataset from the structural shape and size of decision tree induced from the dataset. Totally 15 measures are proposed to describe the structure of a decision tree. Their effectiveness is illustrated through extensive experiments, by comparing to the results obtained by the existing data characteristics techniques, including data characteristics tool (DCT) that is the most wide used technique in meta-learning, and Landmarking that is the most recently developed method.
Translated title of the contributionImproved data set characterisation for meta-learning
Original languageEnglish
Title of host publicationUnknown
Pages141 - 152
Number of pages11
ISBN (Print)3540001883
Publication statusPublished - Jan 2002

Bibliographical note

Conference Proceedings/Title of Journal: Proc. 5th International Conference on Discovery Science (DS-02)


Dive into the research topics of 'Improved data set characterisation for meta-learning'. Together they form a unique fingerprint.

Cite this