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Classiffcation for accuracy and insight: a weighted sum approach

conference contribution
posted on 2007-12-01, 00:00 authored by A Quinn, A Stranieri, John YearwoodJohn Yearwood
This research presents a classiffer that aims to pro-vide insight into a dataset in addition to achieving classiffcation accuracies comparable to other algo-rithms. The classiffer called, Automated Weighted Sum (AWSum) uses a weighted sum approach where feature values are assigned weights that are summed and compared to a threshold in order to classify an example. Though naive, this approach is scalable, achieves accurate classiffcations on standard datasets and also provides a degree of insight. By insight we mean that the technique provides an appreciation of the in o uence a feature value has on class values, rel-ative to each other. AWSum provides a focus on the feature value space that allows the technique to iden-tify feature values and combinations of feature values that are sensitive and important for a classiffcation. This is particularly useful in ffelds such as medicine where this sort of micro-focus and understanding is critical in classiffcation.

History

Volume

70

Pagination

203-208

Location

Gold Coast, Queensland

Start date

2007-12-03

End date

2007-12-04

ISSN

1445-1336

ISBN-13

9781920682514

Language

eng

Publication classification

EN.1 Other conference paper

Copyright notice

2007, Australian Computer Society

Title of proceedings

AusDM 2007 : Proceedings of the 6th Australasian Data Mining Conference 2007

Event

Australasian Data Mining. Conference (6th : 2007 : Gold Coast, Queensland)

Publisher

Australian Computer Society

Series

Conferences in Research and Practice in Information Technology Series

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