A major drawback of version space learning is its inability to deal with noise: any pair of inconsistent examples can cause the version space to collapse, i.e., become empty, so that classification becomes impossible.[1] One solution of this problem is proposed by Dubois and Quafafou that proposed the Rough Version Space,[3] where rough sets based approximations are used to learn certain and possible hypothesis in the presence of inconsistent data.