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An illustration of variable precision rough set theory : The gender classification of the European barn swallow (Hirundo rustica)

journal contribution
posted on 01.09.2003, 00:00 authored by M Beynon, Kate BuchananKate Buchanan
This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.

History

Journal

Bulletin of mathematical biology

Volume

65

Issue

5

Pagination

835 - 858

Publisher

Springer New York LLC

Location

New York, N.Y.

ISSN

0092-8240

eISSN

1522-9602

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2003, Society for Mathematical Biology