Knowledge Measurement Based on Rough Set

In this paper, a method is proposed to measure attribute's importance based on Rough Set theory. According to Rough set theory, knowledge about a universe of objects may be defined as classifications based on certain properties of the objects, i.e. rough set theory assume that knowledge is an ability to partition objects. We quantify the ability of classify objects, and call the amount of this ability as knowledge quantity. The more knowledge quantity the attributes have, the more important they are in the information system. In addition, automatic feature selection methods such as document frequency thresholding (DF), is commonly applied in text categorization, but DF method does not have an academic interpretation, it is usually considered an empirical approach to improve efficiency. We put forward an interpretation of DF based on knowledge quantity.