A novel rough set approach for classification

Rough set theory has been widely and successfully used in data mining, especially in classification field. But most existing rough set based classification approaches require computing optimal attribute reduction, which is usually intractable and many problems related to it have been shown to be NT-hard. Although approximate algorithms exist, they also tend to be computationally expensive. This paper presents a novel rough set method for classification, which does not require computing attribute reduction. It stepwise investigates condition attributes and outputs the classification rules induced by them, which is just like the strategy of "on the fly". The theoretical analysis and the empirical study show that the proposed method is effective and efficient.