C4.5: programs for machine learning
C4.5: programs for machine learning
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
ECML '95 Proceedings of the 8th European Conference on Machine Learning
TimeSleuth: A Tool for Discovering Causal and Temporal Rules
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Discovering temporal/causal rules: a comparison of methods
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Distinguishing causal and acausal temporal relations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
| Hi-index | 0.00 |
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.