Name
Abstract | ||
|---|---|---|
This paper presents a hybrid model for rule discovery in real world data with uncertainty and incompleteness. The hybrid model
is created by introducing an appropriate relationship between deductive reasoning and stochastic process, and extending the
relationship so as to include abduction. Furthermore, a Generalization Distribution Table (GDT), which is a variant of transition
matrix in stochastic process, is defined. Thus, the typical methods of symbolic reasoning such as deduction, induction, and
abduction, as well as the methods based on soft computing techniques such as rough sets, fuzzy sets, and granular computing
can be cooperatively used by taking the GDT and/or the transition matrix in stochastic process as mediums. Ways for implementation
of the hybrid model are also discussed.
|
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0950-7051(01)00153-8 | Knowledge Based Systems |
Keywords | Field | DocType |
rule discovery,fuzzy set,appropriate relationship,hybrid model,soft computing technique,transition matrix,symbolic reasoning,deductive reasoning,stochastic process,granular computing,generalization distribution,generalization distribution table,rough set,soft computing | Decision table,Stochastic matrix,Computer science,Stochastic process,Fuzzy set,Rough set,Granular computing,Artificial intelligence,Deductive reasoning,Soft computing | Journal |
Volume | Issue | ISSN |
14 | 7 | Knowledge-Based Systems |
ISBN | Citations | PageRank |
3-540-43074-1 | 3 | 0.50 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Zhong | 1 | 2907 | 300.63 |
| Juzhen Dong | 2 | 214 | 17.05 |
| Chunnian Liu | 3 | 561 | 61.58 |
| Setsuo Ohsuga | 4 | 960 | 222.02 |