TY - GEN
T1 - The application of adaptive partitioned random search in feature selection problem
AU - Liu, Xiaoyan
AU - Wang, Huaiqing
AU - Xu, Dongming
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - 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. © Springer-Verlag Berlin Heidelberg 2005.
AB - 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. © Springer-Verlag Berlin Heidelberg 2005.
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U2 - 10.1007/11527503_37
DO - 10.1007/11527503_37
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 354027894
SN - 9783540278948
VL - 3584 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 307
EP - 314
BT - Advanced Data Mining and Applications - First International Conference, ADMA 2005, Proceedings
PB - Springer Verlag
T2 - 2005 IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2005
Y2 - 12 September 2005 through 16 September 2005
ER -