ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (7): 1500-1509.doi: 10.7544/issn1000-1239.2017.20160349

• 人工智能 • 上一篇    下一篇

不完备多粒度决策系统的局部最优粒度选择

顾沈明,顾金燕,吴伟志,李同军,陈超君   

  1. (浙江海洋大学数理与信息学院 浙江舟山 316022) (浙江省海洋大数据挖掘与应用重点实验室(浙江海洋大学) 浙江舟山 316022) (gsm@zjou.edu.cn)
  • 出版日期: 2017-07-01
  • 基金资助: 
    国家自然科学基金项目(61602415,61573321,61272021,41631179);浙江省自然科学基金项目(LY14F030001);浙江省海洋科学重中之重学科开放基金项目(20160102)

Local Optimal Granularity Selections in Incomplete Multi-Granular Decision Systems

Gu Shenming, Gu Jinyan, Wu Weizhi, Li Tongjun, Chen Chaojun   

  1. (School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan, Zhejiang 316022) (Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province (Zhejiang Ocean University), Zhoushan, Zhejiang 316022)
  • Online: 2017-07-01

摘要: 粒计算是知识表示和数据挖掘的一个重要方法.从粒计算来看,一个粒是由多个比较小的颗粒组成的更大的一个单元.在许多实际应用中,由于不同标记尺度对数据集进行分割会得到不同层次的粒度,许多人在用粒计算解决问题时自然而然地考虑不同层次的粒度问题.这就促使思考如何选择一个合适的粒度层次来解决问题.围绕不完备多粒度决策系统,研究了基于局部最优粒度的规则提取方法.1)介绍了不完备多粒度决策系统的概念;2)在协调的不完备多粒度决策系统中定义了最优粒度和局部最优粒度、介绍了基于局部最优粒度的属性约简和规则提取方法,在不协调的不完备多粒度决策系统中引入了广义决策、定义了广义最优粒度和广义局部最优粒度,并给出了基于广义局部最优粒度的属性约简和规则提取方法;3)给出了在公开的数据集上的实验结果.

关键词: 决策系统, 粒计算, 局部最优粒度, 多粒度, 规则提取

Abstract: Granular computing is an approach for knowledge representing and data mining. With the view point of granular computing, the notion of a granule is interpreted as one of the numerous small particles forming a larger unit. In many real-life applications, there are different granules at different levels of scale in data sets having hierarchical scale structures. Many people apply granular computing for problem solving by considering multiple levels of granularity. This allows us to focus on solving a problem at the most appropriate level of granularity by ignoring unimportant and irrelevant details. In this paper, rule extraction based on the local optimal granularities in incomplete multi-granular decision systems is explored. Firstly, the concept of incomplete multi-granular decision systems is introduced. Then the notions of the optimal granularity and the local optimal granularity in consistent incomplete multi-granular decision system are defined, and the approaches to attribute reduction and rule extraction based on the local optimal granularities are illustrated; the generalized decisions in inconsistent incomplete multi-granular decision systems are further introduced, the generalized optimal granularity and the generalized local optimal granularity are also defined, and the approaches to attribute reduction and rule extraction based on the generalized local optimal granularities are investigated. Finally, the experimental results on the public datasets are discussed.

Key words: decision system, granular computing, local optimal granularity, multi-granularity, rule extraction

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