Browsing by Author "Wang, Jiandong"

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A Layered-Coevolution-Based Attribute-Boosted Reduction Using Adaptive Quantum-Behavior PSO and Its Consistent Segmentation for Neonates Brain Tissue
(2018-06-01)
Ding, Weiping
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Lin, Chin-Teng
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Prasad, Mukesh
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Cao, Zehong
;
Wang, Jiandong
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.
A more efficient attribute self-adaptive co-evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators
(2015-02-01)
Ding, Weiping
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Guan, Zhijin
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Shi, Quan
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Wang, Jiandong
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腦科學研究中心
In order to further improve the adaptability of attribute reduction and enhance its application performance in large-scale attribute reduction, a more efficient attribute self-adaptive co-evolutionary reduction algorithm by combining quantum elitist frogs and cloud model operators (QECMASCR) is proposed in this paper. Firstly, quantum chromosome is used to encode the evolutionary population, and a multilevel elitist pool of quantum frogs is constructed in which quantum elitist frogs can fast guide the evolutionary population into the optimal area. Secondly, a reversible cloud mode based on attribute entropy weight is designed to adjust the quantum cloud revolving angle, so that the scope of search space can be adaptively controlled under the guidance of qualitative knowledge. In addition, both the quantum cloud mutation operator and quantum cloud entanglement operator are used to make quantum frogs be adaptive to get the optimal set of attribute reduction fast. Thirdly, an improved decomposition framework of attribute self-adaptive co-evolution is adopted to capture interdependencies of decision variables. It can decompose the largescale attribute set into reasonable-scale subsets according to two kinds of the best performance fitness and assignment credit. Thus, some optimal elitists in different memeplexes of multilevel elitist pool are selected out to evolve their representing attribute subsets, which can increase the cooperation and efficiency of attribute reduction. So the global minimum attribute reduction can be achieved steadily and efficiently. Experimental results indicate the proposed QECMASCR algorithm achieves the better superior performance than existing representative algorithms. Moreover it is applied into MRI segmentation, and the effective and robust segmentation results further demonstrate its stronger applicability. (C) 2014 Elsevier Inc. All rights reserved.