引用本文:李雪岩,李学伟,蒋君.基于知识粒度特征的多目标粗糙集属性约简算法[J].控制与决策,2021,36(1):196-205
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基于知识粒度特征的多目标粗糙集属性约简算法
李雪岩1, 李学伟1, 蒋君2
(1. 北京联合大学管理学院,北京100101;2. 北京交通大学经济管理学院,北京100044)
摘要:
针对多知识粒度粗糙集在条件属性权重计算及约简过程中易忽略单个属性序列产生的等价划分的问题,引入帕累托最优思想,同时考虑基于等价关系的知识依赖分辨度以及属性的重要性程度,将多粒度粗糙集属性约简问题转化为离散多目标优化问题.针对该问题的结构设计具有集群智能优化思想及复杂网络拓扑结构的优化算法,在算法中引入基于个体的非支配解集以平衡局部最优与全局最优的关系,引入基于“均值-方差”的遗传算子增加种群多样性.以UCI中的测试数据集作为算例构建粗糙集决策表进行优化计算,引入多种智能算法进行性能比较,依据约简结果,利用多层感知机对数据集中的对象进行分类,验证约简方法的有效性.研究结果表明:所提出方法具有更强的多目标属性挖掘性能;基于帕累托最优思想的多目标属性约简方法能较好地综合知识分辨度与知识粒度建模方式的优点,提升数据集的分类精度.
关键词:  知识粒度  粗糙集  多目标优化  集群智能  复杂网络  多层感知机
DOI:10.13195/j.kzyjc.2019.0490
分类号:O159;O224
基金项目:中国国家铁路集团有限公司科技研究开发计划课题(K2019Z006).
Multi objective rough set attribute reduction algorithm based on characteristics of knowledge granularity
LI Xue-yan1,LI Xue-wei1,JIANG Jun2
(1. School of Management,Beijing Union University, Beijing100101,China;2. School of Economics and Management,Beijing Jiaotong University,Beijing100044,China)
Abstract:
In the process of multi-granulation rough set's weight calculation and the condition attributes reduction, the equivalence partition produced by single attribute is usually ignored. Therefore, the attribute reduction problem of multi-granulation rough sets is transformed into the discrete multi-objective optimization problem by introducing the idea of Pareto optimality, in which both resolution of dependability based on equivalence relation and attributes’ significance are taken into consideration. For this optimization problem, a swarm intelligent optimization algorithm with complex network based population structure is designed, in which the non-dominant solution set for individual is introduced to balance the local optimum and global optimum, and the mean-variance based genetic operator is also designed to increase the diversity of the population. The optimization is conducted based on the rough set decision tables which are obtained from the test data sets in UC Irvine Machine Learning Repository, some other multi objective intelligent algorithms are also introduced as comparison, then based on the reduction results, the multi-layer perceptron is introduced to classify the samples in data sets, and the validity of the proposed algorithm is verified. The results show that: 1) The algorithm proposed in this paper shows better performance in multi objective attribute reduction; 2) The algorithm of multi objective rough set attribute reduction combines the advantage of knowledge resolution and knowledge granularity well, and also improves the classification accuracy of the data sets.
Key words:  knowledge granularity  rough set  multi objective optimization  swarm intelligence  complex network  multi-layer perceptron

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