LFOIL: linguistic rule induction in the label semantics framework. Label semantics is a random set framework for modelling with words. In previous work, several machine learning algorithms based on this framework have been proposed and studied. In this paper, we introduce a new linguistic rule induction algorithm based on Quinlan’s FOIL algorithm. According to this algorithm, a set of linguistic rules is generated for classification problems. The new model is empirically tested on an artificial toy problem and several benchmark problems from UCI repository. The results show that the new model can generate very compact linguistic rules while maintaining comparable accuracy to other well-known data mining algorithms.
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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Zhang, Weifeng; Hu, Hua; Hu, Haiyang; Fang, Jinglong: Semantic distance between vague concepts in a framework of modeling with words (2019)
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- Tang, Yongchuan; Lawry, Jonathan: Information cells and information cell mixture models for concept modelling (2012)
- Tang, Yongchuan; Lawry, Jonathan: A prototype-based rule inference system incorporating linear functions (2010)
- Tan, Shing Chiang; Lim, Chee Peng: Evolutionary fuzzy ARTMAP neural networks and their applications to fault detection and diagnosis (2010) ioport
- Tang, Yongchuan; Lawry, Jonathan: Linguistic modelling and information coarsening based on prototype theory and label semantics (2009)
- Qin, Zengchang; Lawry, Jonathan: LFOIL: linguistic rule induction in the label semantics framework (2008)