不协调区间值决策系统的最大分布约简

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Title:

Maximum distribution reduction in inconsistent interval-valued decision systems

作者:

尹继亮^1,2, 张楠^1,2, 童向荣^1,2, 陈曼如^1,2

1. 烟台大学 数据科学与智能技术山东省高校重点实验室, 山东 烟台 264005;
2. 烟台大学 计算机与控制工程学院, 山东 烟台 264005

Author(s):

YIN Jiliang^1,2, ZHANG Nan^1,2, TONG Xiangrong^1,2, CHEN Manru^1,2

1. Key Lab for Data Science and Intelligence Technology of Shandong Higher Education Institutes, Yantai University, Yantai 264005, China;
2. School of Computer and Control Engineering, Yantai University, Yantai 264005, China

关键词:

分布式约简; 最大分布约简; 置信度; 相容关系; 可辨识矩阵; 不协调; 区间值; 决策系统

Keywords:

distributed reduction; maximum distributed reduction; confidence coefficient; compatibility relation; discernibility matrix; inharmonious; interval-valued; decision system

分类号:

TP181

DOI:

10.11992/tis.201710011

摘要:

分布式约简可以保证约简前后决策系统各规则的置信度保持不变，是属性约简的重要方法之一。最大分布式约简保持了约简前后决策系统中可信程度最大的规则不变，提取置信度较大的规则在智能决策中具有广泛的应用价值。本文在相容关系下的不协调区间值决策系统中引入最大置信度的概念，构造最大分布保持不变的可辨识矩阵，并给出基于可辨识矩阵的最大分布约简算法。分析了不协调区间值决策系统的最大分布约简算法与其它约简算法之间的关系。最后，利用UCI标准数据集进行了实验验证，实验结果表明了算法的有效性。

Abstract:

Distribution reduction is one of the important methods of attribute reduction as it can guarantee consistent confidence coefficients of all decision rules before and after reduction. Maximum distributed reduction keeps the unchanged rule with the highest confidence coefficient in the decision system, and extracting a rule with a high confidence coefficient has a wide application value. This paper introduces the concept of maximum confidence coefficient for inconsistent interval-valued decision systems based on compatibility relation and proposes a maximum distribution reduction algorithm based on discernibility matrix, whereby a discernibility matrix is constructed to keep the unchanged maximum distribution. The relationship between the maximum distribution reduction algorithm in inconsistent interval-valued decision systems and other reduction algorithms was analyzed. Experiments were performed using UCI standard data sets, and the proposed algorithm proved to be effective.