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AWSum - Data mining for insight

conference contribution
posted on 2008-12-01, 00:00 authored by A Quinn, A Stranieri, John YearwoodJohn Yearwood, G Hafen
Many classifiers achieve high levels of accuracy but have limited use in real world problems because they provide little insight into data sets, are difficult to interpret and require expertise to use. In areas such as health informatics not only do analysts require accurate classifications but they also want some insight into the influences on the classification. This can then be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that gives accuracy comparable to other techniques whist providing insight into the data. AWSum achieves this by calculating a weight for each feature value that represents its influence on the class value. The merits of AWSum in classification and insight are tested on a Cystic Fibrosis dataset with positive results. © 2008 Springer-Verlag Berlin Heidelberg.

History

Volume

5139 LNAI

Pagination

524 - 531

Publisher

Springer

Location

Chengdu, China

Place of publication

Berlin, Germany

Start date

2008-10-08

End date

2008-10-10

ISSN

0302-9743

eISSN

1611-3349

ISBN-10

3540881913

Publication classification

EN.1 Other conference paper

Title of proceedings

4th International Conference on Advanced Data Mining and Applications, ADMA 2008

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