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Shared nearest-neighbor quantum game-based attribute reduction with hierarchical coevolutionary spark and its application in consistent segmentation of neonatal cerebral cortical surfaces

Citation

Ding, W and Lin, C-T and Cao, Z, Shared nearest-neighbor quantum game-based attribute reduction with hierarchical coevolutionary spark and its application in consistent segmentation of neonatal cerebral cortical surfaces, IEEE Transactions on Neural Networks and Learning Systems pp. 1-15. ISSN 2162-237X (2018) [Refereed Article]

Copyright Statement

2018 IEEE.

DOI: doi:10.1109/TNNLS.2018.2872974

Abstract

The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.

Item Details

Item Type:Refereed Article
Keywords:attribute reduction, hierarchical coevolutionary spark, neonatal cortical surface segmentation, quantum equilibrium game paradigm (QEGP), shared nearest-neighbor hierarchy, 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:131537
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2019-03-21
Last Modified:2019-05-13
Downloads:0

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