Associative classifier modeling method based on rough set theory and factor analysis technology

This paper presents a classifier modeling technique called RSFAC, by combining on rough set theory and factor analysis technology. Factor analysis technology is introduced to classify the attributes in the dataset at first. Then attribute selection is performed by entropy measure. Thirdly, classification rules are deduced based on rough set analysis, and a classifier is built based on these rules. At the end, new examples can be predicted by a heuristic way. Experimental results show that the classifier established by above approach gets a better prediction than that by some well-known algorithms on some standard datasets.