Name
Abstract | ||
|---|---|---|
Relational rule learning is typically used in solving classification and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individual-centered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications application. |
Year | Venue | Keywords |
|---|---|---|
2002 | ILP | standard ilp problem,prediction task,propositionalization approach,subgroup discovery,relational subgroup discovery,east-west train,relational rule learning,first-order feature construction,individual-centered domain,real-life telecommunications application |
Field | DocType | Volume |
Inductive logic programming,Computer science,First order,Statistical relational learning,Inductive logic,Rule induction,Artificial intelligence,Machine learning | Conference | 2583 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-00567-6 | 31 |
PageRank | References | Authors |
1.88 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nada Lavrač | 1 | 989 | 72.19 |
| Filip Zelezny | 2 | 75 | 9.61 |
| Peter A. Flach | 3 | 3457 | 269.66 |