- Title
- Classification for accuracy and insight : A weighted sum approach
- Creator
- Quinn, Anthony; Stranieri, Andrew; Yearwood, John
- Date
- 2007
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/68187
- Identifier
- vital:3621
- Identifier
- ISBN:1445-1336
- Abstract
- This research presents a classifier that aims to provide insight into a dataset in addition to achieving classification accuracies comparable to other algorithms. The classifier 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 classifications on standard datasets and also provides a degree of insight. By insight we mean that the technique provides an appreciation of the influence a feature value has on class values, relative to each other. AWSum provides a focus on the feature value space that allows the technique to identify feature values and combinations of feature values that are sensitive and important for a classification. This is particularly useful in fields such as medicine where this sort of micro-focus and understanding is critical in classification.
- Publisher
- Gold Coast, Queensland, Victoria : Australian Computer Society
- Relation
- Paper presented at Sixth Australasian Data Mining Conference, AusDM 2007, Gold Coast, Queensland, Victoria : 3rd-4th December 2007 p. 203-208
- Rights
- Open Access
- Rights
- Copyright Australian Computer Society (uploading privileges were granted by permission of the Australian Computer Society Inc)
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0804 Data Format; Data mining; Insight; Conditional probability
- Full Text
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