Name
Abstract | ||
|---|---|---|
The paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to length, coverage and number of misclassifications. Presented algorithm constructs a directed acyclic graph Δγ(T) which nodes are subtables of the decision table T. Based on the graph Δγ(T) we can describe all irredundant γ-decision rules with minimum length, after that among these rules describe all rules with maximum coverage, and among such rules describe all rules with minimum number of misclassifications. We can also change the set of cost functions and order of optimization. Sequential optimization can be considered as tool that help to construct simpler rules for understanding and interpreting by experts. |
Year | Venue | Keywords |
|---|---|---|
2012 | Computer Science and Information Systems | decision tables,decision theory,dynamic programming,graph theory,γ-decision rules,cost functions,decision table,dynamic programming approach,graph theory,sequential optimization |
Field | DocType | ISBN |
Decision rule,Graph theory,Dynamic programming,Decision tree,Optimal decision,Decision table,Computer science,Algorithm,Directed acyclic graph,Artificial intelligence,Decision theory,Machine learning | Conference | 978-83-60810-51-4 |
Citations | PageRank | References |
3 | 0.44 | 18 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beata Zielosko | 1 | 3 | 0.44 |