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- W2365431679 abstract "In order to improve the solution quality and computing efficiency of rough set minimum attribute reduction algorithms based on swarm intelligence,a parallel particle sub-swarm optimization algorithm with long-memory Tabu search capability was proposed.In addition to the taboo restriction,some diversification and intensification schemes were employed.Since parallel sub-swarms have a lower probability of simultaneously getting trapped in a local optimum than a single particle swarm,the proposed algorithm enhances the probability of finding a global optimum and decreases the influence of initial particles.Experimental results on a number of UCI datasets show that the proposed algorithm has a higher probability of finding a minimum attribute reduction in rough sets compared with some existing swarm intelligence based attribute reduction algorithms.Therefore,the proposed algorithm is feasible and relatively effective for the minimum attribute reduction problem." @default.
- W2365431679 created "2016-06-24" @default.
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- W2365431679 date "2011-01-01" @default.
- W2365431679 modified "2023-09-26" @default.
- W2365431679 title "A minimum reduction algorithm based on parallel particle sub-swarm optimization with tabu search capability" @default.
- W2365431679 hasPublicationYear "2011" @default.
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