Name
Abstract | ||
|---|---|---|
Generating decision rule sets from observational data is an established branch of machine learning. Although such rules may be well-suited to machine execution, a human being may have problems interpreting them. Making inferences about the dependencies of a number of attributes on each other by looking at the rules is hard, hence the need to summarize and visualize a rule set. In this paper we propose using dependence diagrams as a means of illustrating the amount of influence each attribute has on others. Such information is useful in both causal and non-causal contexts. We provide examples of dependence diagrams using rules extracted from two datasets. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68825-9_16 | Canadian Conference on AI |
Keywords | Field | DocType |
machine execution,observational data,rule set,dependence diagram,generating decision rule set,machine learning,established branch,non-causal context,decision rule | Decision rule,Data mining,Observational study,Subject-matter expert,Computer science,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
5032.0 | 0302-9743 | 3-540-68821-8 |
Citations | PageRank | References |
2 | 0.43 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamran Karimi | 1 | 118 | 17.23 |
| Howard J. Hamilton | 2 | 1501 | 145.55 |