The application of adaptive partitioned random search in feature selection problem

Liu, Xiaoyan, Wang, Huaiqing and Xu, Dongming (2005). The application of adaptive partitioned random search in feature selection problem. In: X. Li, S. Wang and Z.Y. Dong, Advanced Data Mining and Applications: First International Conference, ADMA 2005: Proceedings. 1st International Conference on Advanced Data Mining and Applications (ADMA 2005), Wuhan, China, (307-314). 22-24 July 2005. doi:10.1007/11527503_37


Author Liu, Xiaoyan
Wang, Huaiqing
Xu, Dongming
Title of paper The application of adaptive partitioned random search in feature selection problem
Conference name 1st International Conference on Advanced Data Mining and Applications (ADMA 2005)
Conference location Wuhan, China
Conference dates 22-24 July 2005
Proceedings title Advanced Data Mining and Applications: First International Conference, ADMA 2005: Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2005
Sub-type Fully published paper
DOI 10.1007/11527503_37
ISBN 9783540278948
354027894X
ISSN 0302-9743
1611-3349
Editor X. Li
S. Wang
Z.Y. Dong
Volume 3584
Start page 307
End page 314
Total pages 8
Language eng
Abstract/Summary Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.
Subjects 749999 Education and training not elsewhere classified
B1
1503 Business and Management
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Tue, 14 Aug 2007, 12:04:57 EST