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Abstract

Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.

Item Type:	Article
Authors/Creators:	Ding, W and Lin, C-T and Cao, Z
Keywords:	classification of infant fMRI, deep neurocognitive co-evolution, fuzzy attribute reduction, minimize interdependent variables, quantum leaping particle swarm optimization (PSO) with nearest-neighbor memeplexes, neural networks
Journal or Publication Title:	IEEE Transactions on Cybernetics
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	2168-2267
DOI / ID Number:	https://doi.org/10.1109/TCYB.2018.2834390
Copyright Information:	Copyright 2018 IEEE.
Related URLs:	Google Scholar: Cao, Z
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