DING Weiping, GUAN Zhijin, WANG Jiehua, TIAN Di. A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI[J]. Chinese Journal of Electronics, 2017, 26(6): 1168-1176. doi: 10.1049/cje.2017.01.004
Citation: DING Weiping, GUAN Zhijin, WANG Jiehua, TIAN Di. A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI[J]. Chinese Journal of Electronics, 2017, 26(6): 1168-1176. doi: 10.1049/cje.2017.01.004

A Layered Co-evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D-MRI

doi: 10.1049/cje.2017.01.004
Funds:  This work is supported by the National Natural Science Foundation of China (No.61300167), Natural Science Foundation of Jiangsu Province (No.BK20151274), Qing Lan Project of Jiangsu Province, the Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University (No.93K172016K03), the Open Project Program of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (No.JYB201606), and Six Talent Peaks Project of Jiangsu Province (No.XYDXXJS-048).
  • Received Date: 2015-12-30
  • Rev Recd Date: 2016-05-20
  • Publish Date: 2017-11-10
  • As the conventional feature selection algorithms are prone to the poor running efficiency in largescale datasets with interacting features, this paper aims at proposing a novel rough feature selection algorithm whose innovation centers on the layered co-evolutionary strategy with neighborhood radius hierarchy. This hierarchy can adapt the rough feature scales among different layers as well as produce the reasonable decompositions through exploiting any correlation and interdependency among feature subsets. Both neighborhood interaction within layer and neighborhood cascade between layers are adopted to implement the interactive optimization of neighborhood radius matrix, so that both the optimal rough feature selection subsets and their global optimal set are obtained efficiently. Our experimental results substantiate the proposed algorithm can achieve better effectiveness, accuracy and applicability than some traditional feature selection algorithms.
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