Name
Abstract | ||
|---|---|---|
The paper describes two soft induction techniques, GDT-NR and GDT-RS, for discovering classification rules from databases with uncertainty and incompleteness. The techniques are based on a generalization distribution table (GDT), in which the probabilistic relationships between concepts and instances over discrete domains are represented. By using the GDT as a probabilistic search space, (1) unseen instances can be considered in the rule discovery process and the uncertainty of a rule, including its ability to predict unseen instances, can be explicitly represented in the strength of the rule; (2) biases can be flexibly selected for search control and background knowledge can be used as a bias to control the creation of a GDT and the rule discovery process. We describe that a GDT can be represented by a variant of connectionist networks (GDT-NR for short), and rules can be discovered by learning on the GDT-NR. Furthermore, we combine the GDT with the rough set methodology (GDT-RS for short). By using GDT-RS, a minimal set of rules with larger strengths can be acquired from databases with noisy, incomplete data. We compare GDT-NR with GDT-RS, and describe GDT-RS is a better way than GDT-NR for large, complex databases. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0925-2312(00)00341-6 | Neurocomputing |
Keywords | Field | DocType |
Inductive learning,Knowledge discovery,Generalization distribution table (GDT),Rough sets,Soft computing,Uncertainty and incompleteness,Background knowledge | Pattern recognition,Rough set,Knowledge extraction,Artificial intelligence,Soft computing,Probabilistic logic,Business process discovery,Connectionism,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
36 | 1 | 0925-2312 |
Citations | PageRank | References |
13 | 1.09 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Zhong | 1 | 2907 | 300.63 |
| Juzhen Dong | 2 | 214 | 17.05 |
| Setsuo Ohsuga | 3 | 960 | 222.02 |