|Table of Contents|

An Attribute Reduction Update Algorithm for Object’sAdding-Deleting Based on Rough Set Theory(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2015年01期
Page:
48-
Research Field:
计算机科学
Publishing date:

Info

Title:
An Attribute Reduction Update Algorithm for Object’sAdding-Deleting Based on Rough Set Theory
Author(s):
Lu You; Hua Ze; Xi Xuefeng; Zhang Ni; Wu Hongjie
School of Electronical and Information Engineering,Suzhou University of Science and Technology,Suzhou 215000,China
Keywords:
rough set; incremental data mining; reduction of attribute
PACS:
TP18
DOI:
-
Abstract:
Attribute reduction is one of the important topics in the research on rough set theory. When an object was added to or deleted from the original decision table,how to calculate attribute reduction fast and effectively is a pressing problem. This paper proposed an attribute reduction update algorithm. Firstly,the changing mechanism of conditional entropy was analyzed when object is added to or removed from the table,and then we divided the added or removed objects into different cases. Furthermore,we presented the update algorithm based on these cases and implemented it based on hash table. Experiment results show that our algorithm can calculate the attribute reduction fast and outperforms the existing methods.

References:

[1] Kim H,Claffy K,Fomenkov M,et al. Internet traffic classification demystified:myths,caveats,and the best practices[C]//Proceedings of ACM CoNEXT’08. New York,2008:1-12.
[2]Zhang D,Qiu J,Li X. Attribute reduction based on equivalence classes with multiple decision values in rough set[C]//Proceedings of the International Conference on Information Engineering and Applications(IEA)2012. London:Springer,2013:505-512.
[3]Jia X,Tang Z,Liao W,et al. On an optimization representation of decision-theoretic rough set model[J]. International Journal of Approximate Reasoning,2014,55(1):156-166.
[4]陆悠,罗军舟,李伟,等. 面向网络状态的自适应用户行为评估方法[J]. 通信学报,2013(7):71-80.
[5]钱文彬,杨炳儒,徐章艳. 基于信息熵的核属性增量式高效更新算法[J]. 模式识别与人工智能,2013,26(1):42-49.
[6]Janusz A,S’lzak D. Rough set methods for attribute clustering and selection[J]. Applied Artificial Intelligence,2014,28(3):220-242.

Memo

Memo:
-
Last Update: 2015-03-30