Heuristic method for attribute selection from partially uncertain data using rough sets
In this paper, we deal with the problem of attribute selection from partially uncertain data based on rough sets without costly calculation. The uncertainty exists in decision attributes and is represented by the transferable belief model, one interpretation of the belief function theory. To solve this problem, we propose a heuristic method for attribute selection able to extract the more relevant features needed in the classification process. The simplification of the uncertain decision table using this heuristic method yields to learn simplified and more significant belief decision rules in a quick time. The experiments show interesting results based on two evaluation criteria such as the accuracy classification and the time complexity.
heuristic attribute selection rough sets uncertainty belief function theory classification