RRIA: A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm

Issue title: The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)

Article type: Research Article

Authors: Zheng, Zheng | Wang, Guoyin

Affiliations: Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, R.P. China

Abstract: As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our 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, the simulation results also show that our algorithm outperforms ID4 in many aspects.

Keywords: rough set, rule tree, incremental learning, knowledge acquisition, data mining

Journal: Fundamenta Informaticae, vol. 59, no. 2-3, pp. 299-313, 2004