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A layered-coevolution-based attribute-boosted reduction using adaptive quantum-behavior pso and its consistent segmentation for neonates brain tissue

Citation

Ding, W and Lin, C-T and Prasad, M and Cao, Z and Wang, J, A layered-coevolution-based attribute-boosted reduction using adaptive quantum-behavior pso and its consistent segmentation for neonates brain tissue, IEEE Transactions on Fuzzy Systems, 26, (3) pp. 1177-1191. ISSN 1063-6706 (2018) [Refereed Article]


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Copyright Statement

2017 IEEE

DOI: doi:10.1109/TFUZZ.2017.2717381

Abstract

The main challenge of attribute reduction in large data applications is to develop a new algorithm to deal with large, noisy, and uncertain large data linking multiple relevant data sources, structured or unstructured. This paper proposes a new and efficient layered-coevolution-based attribute-boosted reduction algorithm (LCQ-ABR*) using adaptive quantum-behavior particle swarm optimization (PSO). First, the quantum rotation angle of an evolutionary particle is updated by a dynamic change of self-adapting step size. Second, a self-adaptive partitioning strategy is employed to group particles into different memeplexes, and the quantum-behavior mechanism with the particles' states depicted by the wave function cooperates to achieve superior performance in their respective memeplexes. Third, a new layered coevolutionary model with multiagent interaction is constructed to decompose a complex attribute set, and it can self-adapt the attribute sizes among different layers and produce the reasonable decompositions by exploiting any interdependence among multiple relevant attribute subsets. Fourth, the decomposed attribute subsets are evolved to compute the positive region and discernibility matrix by using their best quantum particles, and the global optimal reduction set is induced successfully. Finally, extensive comparative experiments are provided to illustrate that LCQ-ABR* has better feasibility and effectiveness of attribute reduction on large-scale and uncertain dataset problems with complex noise as compared with representative algorithms. Moreover, LCQ-ABR* can be successfully applied in the consistent segmentation for neonatal brain three-dimensional MRI, and the consistent segmentation results further demonstrate its stronger applicability.

Item Details

Item Type:Refereed Article
Keywords:adaptive quantum-behavior particle swarm optimization (PSO), attribute-boosted reduction, consistent segmentation for neonates brain tissue, layered coevolution with multiagent interaction, sulci and gyrus estimate, fuzzy neural networks
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Expert Systems
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence
UTAS Author:Cao, Z (Mr Zehong Cao)
ID Code:131541
Year Published:2018
Web of Science® Times Cited:18
Deposited By:Information and Communication Technology
Deposited On:2019-03-21
Last Modified:2019-04-29
Downloads:3 View Download Statistics

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