Sequential covering rule induction algorithm for variable consistency rough set approaches

We present a general rule induction algorithm based on sequential covering, suitable for variable consistency rough set approaches. This algorithm, called VC-DomLEM, can be used for both ordered and non-ordered data. In the case of ordered data, the rough set model employs dominance relation, and in the case of non-ordered data, it employs indiscernibility relation. VC-DomLEM generates a minimal set of decision rules. These rules are characterized by a satisfactory value of the chosen consistency measure. We analyze properties of induced decision rules, and discuss conditions of correct rule induction. Moreover, we show how to improve rule induction efficiency due to application of consistency measures with desirable monotonicity properties.