On data and algorithms: Understanding inductive performance

• Type: Article in International Scientific Journal
• Date: 2004

Authors

Alexandre Kalousis (Author) Other; João Gama (Author) FEP; Melanie Hilario (Author) Other

Journal

Title: Machine Learning

Vol. 54 No. 3

Pages: 275-312

ISSN: 0885-6125

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 46 Citations

ISI Web of Science - 37 Citations

Scopus - 68 Citations

Scientific classification

FOS: Natural sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-000-BFK

DOI: 10.1023/b:mach.0000015882.38031.85

Abstract (EN): In this paper we address two symmetrical issues, the discovery of similarities among classification algorithms, and among datasets. Both on the basis of error measures, which we use to define the error correlation between two algorithms, and determine the relative performance of a list of algorithms. We use the first to discover similarities between learners, and both of them to discover similarities between datasets. The latter sketch maps on the dataset space. Regions within each map exhibit specific patterns of error correlation or relative performance. To acquire an understanding of the factors determining these regions we describe them using simple characteristics of the datasets. Descriptions of each region are given in terms of the distributions of dataset characteristics within it.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 38