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ADMA
2010
Springer

Classification Inductive Rule Learning with Negated Features

10 years 25 days ago
Classification Inductive Rule Learning with Negated Features
This paper reports on an investigation to compare a number of strategies to include negated features within the process of Inductive Rule Learning (IRL). The emphasis is on generating the negation of features while rules are being "learnt"; rather than including (or deriving) the negation of all features as part of the input. Eight different strategies are considered based on the manipulation of three feature sub-spaces. Comparisons are also made with Associative Rule Learning (ARL) in the context of multi-class text classification. The results indicate that the option to include negated features within the IRL process produces more effective classifiers. Key words: Rule Learning, Negation, Multi-class Text Classification
Stephanie Chua, Frans Coenen, Grant Malcolm
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ADMA
Authors Stephanie Chua, Frans Coenen, Grant Malcolm
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