Abstract
Rough set theory is emerging as a new tool for dealing with fuzzy and uncertain data. It is very efficient to analyse and process imprecise and imperfect data. It can find potential knowledge and rule from data. In recent years,it has been studied and applied in many fields such as machine learning,data mining and etc.
Knowledge acquision is one of the most important part of rough set,and it is also the focus of researching. There are some problems in knowledge acquisition that need to be solved. We deal with two key problems in this paper,that is,computation of attribute core and incremental aquisition of knowledge.
Attribute reduction is one of the most important parts in knowledge acquisition. The core attributes of a decision table are often the start and key of many information reduction procedures. Hu’s method of computating core attributes based on discernibility matrix was wrong because of ignoring some factors. The error was corrected by Ye Dongyi,but his alogrithm’s complexity was too high. In this paper,we present a new algorithm based on objects,combination. The algorithm corrects Hu’s error and its complexity is lower than Ye’s method and Hu’s method.
In resent years,many rough set based algorithms for computing the smallest or smaller reduction of attributes and knowledge acquisition are developed[1~3]. They are almost based on static data. However,real databases are always dynamic. So,many researchers[4~6] suggest that knowledge acquisition in databases should be incremental. Incremental arithmetic for the smallest reduction of attributes and incremental algorithm of rule extraction based on concept lattice have been developed,but there are few incremental rough set based algorithms about knowledge acquisition. On the basis of former results,we develop a rough set and rule tree based incremental knowledge acquisition algorithm (RRIA) in this paper. Simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms,and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides,we compare our algorithm with ID4 algorithm. The results show that the rule quality and the recognition rate of our algorithm are both better than ID4.
Key words: Rough Set Core Attribute Knowledge Reduction Discernibility Matrix Rough set Independent learning Knowledge acquisition