Rough set attributes reduction based on adaptive PBIL algorithm

Author

Wang, Lihua ; Ma, Liangli ; Bian, Qiang ; Zhao, Xiliang

Author_Institution

Dept. of Comput. Eng., Naval Univ. of Eng., Wuhan, China

fYear

2010

fDate

17-19 Dec. 2010

Firstpage

21

Lastpage

24

Abstract

This paper presents a PBIL algorithm based on adaptive theory-giving that the traditional reduction of rough set is not unique and the process lasts for a long time. The learn probability and mutation rate of traditional PBIL algorithm can change adaptively by introducing the Systemic Entropy, then a self-learning and adaptive variability PBIL algorithm (APBIL) is formed. When it is applied to attributes reduction of rough set, it not only maintains the characteristics of global optimization but also reduces the correlation among attributes. Finally, the simplicity and effectiveness of the algorithm are demonstrated by an example.

Keywords

adaptive systems; learning (artificial intelligence); probability; rough set theory; adaptive theory; adaptive variability PBIL algorithm; learn probability; mutation rate; rough set attribute reduction; self-learning; systemic entropy; Algorithm design and analysis; Classification algorithms; Entropy; Gallium; Heuristic algorithms; Optimization; Rough sets; PBIL algorithm; Rough set; adaptive;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory and Information Security (ICITIS), 2010 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-6942-0

Type

conf

DOI

10.1109/ICITIS.2010.5689639

Filename

5689639