RULE INDUCTION FROM NUMERICAL DATA BASED ON ROUGH SETS THEORY
To induce rules from numerical data by rough sets, there are two kinds of methods. One is to discretize the original data and then apply the crisp rough sets models. Here the rough sets models which can only deal with the nominal data are called crisp rough sets models. The other is to fuzzify the original data and then apply fuzzy rough sets models. There are some problems on both of these methods on rules induction such as information loss after discretization or increasing of data size after fuzzification. In this paper we make an attempt to propose one method to induce rules without diseretization or fuzzification. Firstly the indiscernibility relation which is the underlining concept of rough sets is redefined as the similarity relation. Subsequently,the concepts of knowledge reduction are proposed based on the similarity relation. Finally, the numerical experiments show that our method is feasible and effective.
Knowledge reduction rule induction rough sets fuzzy similarity matrix fuzzy significance of attributes
SU-YUN ZHAO WING W.Y.NG ERIC C.C.TSANG DANIEL S.YEUNG DE-GANG CHEN
Department of computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Department of Mathematics and Physics, North China Electric Power University (Beijing), 102206
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2294-2299
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）