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.
Rule Learning Negation Multi-class Text Classification
Stephanie Chua Prans Coenen Grant Malcolm
Department of Computer Science,University of Liverpool Ashton Building,Ashton Street L69 3BX Liverpool UK
国际会议
6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)
重庆
英文
125-136
2010-11-19(万方平台首次上网日期,不代表论文的发表时间)