DATA REDUCTION THROUGH COMBINING LATTICE WITH ROUGH SETS
In this paper, we propose a new efficient data reduction algorithm through combining lattice with rough set. On the basis of lattice learning, the algorithm applies the concept of attribute reduction in the theory of Rough Sets and calculates the importance degree of attributes automatically by a density based approach. Under acceptable classification precision and complexity, it reduces row and column together and generates concise classification rules. The algorithm represents a solution to the problem of attribute generalization on the basis of lattice learning and automatic estimation of attribute weights independently of domain experts. Attributes in the classification rules are ordered by the importance degree of attribute. So in the classification and by the sequence of importance degree of attribute, from one attribute to another,we can exclude the objects which dissatisfy the constraint from the attribute. And then it can, to a large extent, reduces the size of data set of object classified by scanning attribute of the rules, and thereby the efficiency of classification is improved greatly.
Data mining lattice lattice machine rough set hypertuple weight automatic evaluation
BAO-CHENG SU JIAN-CHAO XU SHU-YAN CHEN ZHI-PING LI
Academy of Computer Science & Engineering, Changchun University of Technology, Changchun 130012, Chi Academy of Computer Science & Engineering, Changchun University of Technology, Changchun 130012, Chi
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
990-995
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）