The ability to team from empirical data or the observation of a real world
and accurately predict future instances is an important feature expected in
human. It allows knowledge to be gained by experience and decision rules i
nduced from empirical data. One of the major obstacles in performing rule i
nduction from empirical data is the inconsistency of information about a pr
oblem domain. Rough set theory provides a novel way of dealing with vaguene
ss and uncertainty. When coupled with genetic algorithms, a rule induction
engine that is able to induce probable rules from inconsistent information
can possibly be developed. This paper presents an integrated approach that
combines rough set theory, genetic algorithms and Boolean algebra, for indu
ctive learning. Using such an approach, a prototype system (RClass-Plus) th
at discovers rules from inconsistent empirical data, has been developed. Th
e system was validated using the data obtained from a case study. The resul
ts of the validation are presented. (C) 2001 Elsevier Science B.V. All righ
ts reserved.