Parallel Fuzzy Cognitive Map Using Evolutionary Feature Reduction for Big Data Classification Problem

Judy, M. V.; Soman, Gayathri

doi:10.1007/978-981-13-1343-1_22

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Ruan et al developed the belief-degree-distributed fuzzy cognitive maps (BDD-FCMs), where causal connections were represented by a belief structure [22]. In 2018, a fuzzy cognitive map model utilising map-reduce was introduced, presenting a parallel fuzzy cognitive map approach [23]. Choi et al proposed a big-data-driven fuzzy cognitive map model to handle big datasets using a fuzzy cognitive map [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification

et al. 2023

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This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods.

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Section: Literature Reviewmentioning

confidence: 99%