A novel classification algorithm based on the rough set concepts of fuzzy lower and upper approximations is proposed. The algorithm transforms each quantitative value of a feature into fuzzy sets of linguistic terms using membership functions and calculates the fuzzy lower and upper approximations. The membership functions are generated from cluster points generated by the subtractive clustering technique. A certain rule set based on fuzzy lower approximation and a possible rule set based on fuzzy upper approximation are generated. A genetic algorithm, based on iterative rule learning in combination with a boosting technique, is used to generate the possible rules. The proposed classifier is tested with three well known datasets from the UCI machine learning repository, and compared with relevant classification methods.