Learning coverage rules from incomplete data based on rough sets

In this paper, we deal with the problem of producing a set of certain and possible rules for coverage of incomplete data sets based on rough sets. All the coverage rules gathered together can cover all the given training examples. Unknown values are first assumed to be any possible values and are gradually refined according to the incomplete lower and upper approximations derived from the given incomplete training examples. One of the best equivalence classes in incomplete lower or upper approximations is chosen according to some criteria. The objects covered by the incomplete equivalence class are then removed from the incomplete training set. The same procedure is repeated to find the coverage set of rules. The training examples and the approximations then interact on each other to find the maximally general coverage rules and to estimate appropriate unknown values. The rules derived can then be used to build a prototype knowledge base.