Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.fss.2007.10.008 | Fuzzy Sets and Systems |
Keywords | Field | DocType |
foil algorithm,linguistic rule induction,well-known data mining algorithm,random set framework,new model,artificial toy problem,uci repository,new linguistic rule induction,linguistic rule,label semantics framework,benchmark problem,compact linguistic rule,foil,machine learning | Information processing,Toy problem,Computer science,Fuzzy set,Rule induction,Artificial intelligence,Fuzzy control system,Data mining algorithm,Linguistics,Semantics,Machine learning | Journal |
Volume | Issue | ISSN |
159 | 4 | Fuzzy Sets and Systems |
Citations | PageRank | References |
18 | 0.74 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zengchang Qin | 1 | 439 | 45.46 |
| Jonathan Lawry | 2 | 172 | 19.06 |