In this paper we develop a rough set-based solution to dealing with the inconsistent decision classes of instances in inducing decision rules and an evidential reasoning method to resolve such inconsistent conclusions encountered in determining class decisions for instances. The distinguishing aspects of our method are to exploit the rough boundary region in inducing rules and to aggregate multiple conclusions in classifying instances. We present our proposed method and use an example to illustrate how our method can be applied to classification problems along with its advantage.
|Title of host publication||Interval / Probabilistic Uncertainty and Non-Classical Logics Advances in Soft Computing|
|Publication status||Published - 2008|
Bi, Y., Shen, X., & Wu, S. (2008). Uncertainty Reasoning in Rough Knowledge Discovery. In Interval / Probabilistic Uncertainty and Non-Classical Logics Advances in Soft Computing (pp. 187-200). Springer.