A Layered Co‐evolution Based Rough Feature Selection Using Adaptive Neighborhood Radius Hierarchy and Its Application in 3D‐MRI. Issue 6 (1st November 2017)

• Record Type: Journal Article
• Authors: Ding, Weiping; Guan, Zhijin; Wang, Jiehua; Tian, Di
• Is Part Of: Chinese journal of electronics. Volume 26:Issue 6(2017)
• Page Start: 1168
• Page End: 1176
• Publication Date: 2017-11-01
• Subjects: Layered co‐evolution -- Rough feature selection -- Adaptive neighborhood radius hierarchy -- Longitudinal cortical surface labeling; biomedical MRI -- feature selection -- medical image processing; layered coevolution based rough feature selection -- adaptive neighborhood radius hierarchy -- 3D‐MRI -- neighborhood interaction -- neighborhood cascade -- interactive optimization -- global optimal set; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2017.01.004
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.