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On learning algorithm selection for classification

journal contribution
posted on 06.12.2017, 00:00 by A B M Shawkat AliA B M Shawkat Ali, K Smith
This paper introduces a new method for learning algorithm evaluation and selection, with empirical results based on classification. The empirical study has been conducted among 8 algorithms/classifiers with 100 different classification problems. We evaluate the algorithms’ performance in terms of a variety of accuracy and complexity measures. Consistent with the No Free Lunch theorem, we do not expect to identify the single algorithm that performs best on all datasets. Rather, we aim to determine the characteristics of datasets that lend themselves to superior modelling by certain learning algorithms. Our empirical results are used to generate rules, using the rule-based learning algorithm C5.0, to describe which types of algorithms are suited to solving which types of classification problems. Most of the rules are generated with a high confidence rating.

History

Volume

6

Issue

2

Start Page

119

End Page

138

Number of Pages

20

ISSN

1568-4946

Location

Netherlands

Publisher

Elsevier

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Monash University;

Era Eligible

Yes

Journal

Applied soft computing.

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