Title :
A Novel Attribute Reduction Algorithm Based Improved Differential Evolution
Author :
Yan, Hongwen ; Li, Xinran
Author_Institution :
Dept. of Electr. Eng., Hunan Univ., Changsha, China
Abstract :
One important application of rough sets theory is that of attributes reduction in databases, Solving minimum attribute reduction by differential evolution algorithm is a new research direction. In this paper, an improved differential evolution algorithm and a new definition form of fitness function are present. A attribute reduction algorithm which can remove superfluous attributes without changing the original based on the improved differential evolutionary algorithm is proposed. Simulation experiments and a comparative analysis with an existing algorithm are carried out with multiple sets of data. Experimental results show that the algorithm is effective and can quickly converge to the global optimal solution.
Keywords :
data reduction; evolutionary computation; rough set theory; attribute reduction algorithm; databases; differential evolution algorithm; rough sets theory; Algorithm design and analysis; Databases; Evolutionary computation; Frequency modulation; Information systems; Optimization; Rough sets; attribute reduction; differential evolution algorithm; population; rough sets;
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
DOI :
10.1109/GCIS.2010.103