Open AccessA Layered Co‐evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D‐MRI
Author(s) -
Ding Weiping,
Guan Zhijin,
Wang Jiehua,
Tian Di
Publication year - 2017
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.01.004
Subject(s) - hierarchy , radius , feature selection , computer science , feature (linguistics) , selection (genetic algorithm) , pattern recognition (psychology) , artificial intelligence , hydrodynamic radius , statistical physics , biological system , mathematics , materials science , physics , biology , nanotechnology , dynamic light scattering , linguistics , philosophy , computer security , economics , nanoparticle , market economy
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.