Heuristic method for attribute selection from partially uncertain data using rough sets

Abstract

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.

Keywords:

• uncertainty
• belief function theory
• rough sets
• heuristic
• attribute selection
• classification

Acknowledgements

The author would like to thank Richard Jensen for his help and for the interesting discussions with him during the writing of this paper. Many thanks are also due to anonymous referees for their detailed comments, criticisms and suggestions that have helped greatly in improving the quality of this paper.

Notes

^1. http://www.ics.uci.edu/mlearn/MLRepository.html.