A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Attribute Core of Decision Table
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Fundamenta Informaticae
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Topological approaches to covering rough sets
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
A hierarchical model for test-cost-sensitive decision systems
Information Sciences: an International Journal
Journal of Artificial Intelligence Research
Positive approximation: An accelerator for attribute reduction in rough set theory
Artificial Intelligence
Optimal sub-reducts with test cost constraint
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
Hi-index | 0.00 |
Test cost is an important issue in cost-sensitive systems. It is what we pay for obtaining a data item of an object. In some applications, there are some common costs and a cost constraint. The common cost is due to the share of the same resources by several tests. The cost constraint is due to limited money, time, or other resources. Recently, the two issues have been addressed independently in cost-sensitive rough sets. In contrast, this paper considers both issues. Our problem is to construct test sets meeting the constraint and preserving the information of decision systems to the highest degree. We propose a heuristic algorithm to deal with this problem. It is based on information gain, test costs, group-memberships, common costs and a non-positive exponent λ. λ is employed in the penalty function such that expensive tests are unlikely to be chosen. Experimental results indicate that the algorithm performs good in terms of the possibility of finding the optimal reduct. Since the optimal setting of λ is often unknown, we can run the algorithm with different λ values and obtain better results.