Name
Abstract | ||
|---|---|---|
Pattern structures allow one to approach the knowledge extraction problem in case of arbitrary object descriptions. They provide the way to apply Formal Concept Analysis (FCA) techniques to non-binary contexts. However, in order to produce classification rules a concept lattice should be built. For non-binary contexts this procedure may take much time and resources. In order to tackle this problem, we introduce a modification of the lazy associative classification algorithm and apply it to credit scoring. The resulting quality of classification is compared to existing methods adopted in bank systems. |
Year | Venue | Field |
|---|---|---|
2015 | FCA4AI@IJCAI | Associative property,Computer science,Artificial intelligence,Knowledge extraction,Formal concept analysis,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexey Masyutin | 1 | 0 | 1.35 |
| Yury Kashnitsky | 2 | 1 | 3.44 |
| Sergei O. Kuznetsov | 3 | 1630 | 121.46 |