Dynamic Grade on the Major Hazards Using Community Detection Based on Genetic Algorithm

Grade on the major hazards is of great importance to industry. But the already proposed methods are not fit in with the precision we need. In this paper, a novel method is proposed for dynamic grade on Major Hazards using Community Detection in complex networks (namely MHCD). Firstly MHCD represents the input data as a network, and then uses a novel evolutionary algorithm to find the communities in such a network. Each detected community corresponds to a specific risk grade. In this work we introduce a new generalized method for transformation of the input data to network, and propose a novel evolutionary algorithm to detect the communities. The results of the simulation experiment on a practical problem show that compared with other classification methods, MHCD has better performances.

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