Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Algorithm portfolios based on cost-sensitive hierarchical clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
We present modification of the ProbRough algorithm for inducing decision rules from data. The generated rough classifiers are now sensitive to costs varying from object to object in the training data. The individual costs are represented by new cost attributes defined for every single decision. In this approach the decision attribute is dispensable. Grouping of objects and defining prior probabilities are made on the basis of the group attribute. Values of this attribute may have no relations with the decisions. The proposed approach is a generalization of the methodology incorporating the cost matrix. Behavior of the algorithm is illustrated on the data concerning the credit evaluation task.