Improved dataset characterisation for meta-learning

• Type: Article in International Scientific Journal
• Date: 2002

Authors

Peng, YH (Author) Other; Flach, PA (Author) Other; Soares, C (Author) FEP; Brazdil, P (Author) FEP

Journal

Title: Lecture Notes in Computer Science

Vol. 2534

Pages: 141-152

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 88 Citations

Scopus - 109 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-000-R2R

DOI: 10.1007/3-540-36182-0_14

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12