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首页> 外文会议>Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation >Rough set approximate entropy reducts with order based particle swarm optimization
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Rough set approximate entropy reducts with order based particle swarm optimization

机译：基于阶的粒子群优化算法的粗糙集近似熵约简

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摘要

We propose an order-based Particle Swarm Optimization (o-PSO) hybrid algorithm for rough set approximate entropy reducts (oPSOAER). The o-PSO generates proper permutation of attributes, which are used by approximate entropy reduction algorithm to produce rough set reducts. The reducts are evaluated by fitness function. The primary criterion of optimization of the fitness function is the number of rules and the secondary is the reduct length. Our algorithm is tested on some UCI datasets. The results show that oSPOAER is efficient for approximate entropy reducts. The approximate entropy reducts optimized according to number of rules are better in classification algorithms than the shortest ones, and are much better for practical applications.
机译：我们提出了一种基于订单的粒子群优化（o-PSO）混合算法，用于粗糙集近似熵的归约（oPSOAER）。 o-PSO生成适当的属性置换，近似熵约简算法将其用于产生粗糙集约简。减少量通过适应度函数进行评估。优化适应度函数的主要标准是规则的数量，其次是还原长度。我们的算法已在一些UCI数据集上进行了测试。结果表明，oSPOAER对于减少近似熵是有效的。在分类算法中，根据规则数量优化的近似熵约简比最短的约简更好，并且在实际应用中也更好。

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