基于偏好不一致熵的有序决策

doi:10.11772/j.issn.1001-9081.2017.03.796

计算机应用 ›› 2017, Vol. 37 ›› Issue (3): 796-800.DOI: 10.11772/j.issn.1001-9081.2017.03.796

基于偏好不一致熵的有序决策

潘伟^1,2, 佘堃¹

1. 电子科技大学信息与软件工程学院, 成都 610054;
2. 西华师范大学计算机学院, 四川南充 637009

收稿日期:2016-08-26 修回日期:2016-10-14 出版日期:2017-03-10 发布日期:2017-03-22
通讯作者: 潘伟
作者简介:潘伟(1976-),男,四川武胜人,副教授,博士研究生,主要研究方向:粗糙集、粒计算、云计算和知识发现;佘堃(1967-),男,四川成都人,教授,博士生导师,博士,CCF会员,主要研究方向:智能云、安全云、大数据。
基金资助:
四川省教育厅自然科学基金重点资助项目（12ZA178）；四川省重大项目支撑计划项目（2015GZ0102）；四川省可视计算和虚拟现实重点实验室建设基金资助项目（KJ201406）。

Ordered decision-making based on preference inconsistence-based entropy

PAN Wei^1,2, SHE Kun¹

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu Sichuan 610054, China;
2. Computer School, China West Normal University, Nanchong Sichuan 637009, China

Received:2016-08-26 Revised:2016-10-14 Online:2017-03-10 Published:2017-03-22
Supported by:
This work is partially supported by the Natural Science Foundation of the Education Department of Sichuan Province (12ZA178), the Key Technology Support Program of Sichuan Province(2015GZ0102), the Foundation of Visual Computing and Virtual Reality Key Laboratory of Sichuan Province (KJ201406).

摘要/Abstract

摘要： 针对多规则有序决策系统中的偏好决策问题，根据有序决策的偏好不一致特性，提出了一种基于偏好不一致熵的偏好决策方法。首先，定义了样本的偏好不一致熵（PIEO），用来度量特定样本相对于样本集的偏好不一致程度；然后，根据偏好决策中不同属性对决策的重要性不同的特点，提出了一种加权的样本偏好不一致熵，并结合属性偏好不一致熵在度量属性重要性方面的能力，给出了一种基于属性偏好不一致熵的权值的计算方法；最后，提出了一种基于样本偏好不一致熵的偏好决策算法。采用Pasture Production和Squalsh两个数据集进行仿真实验，基于全局偏好不一致熵分类后，各属性的偏好不一致熵普遍比基于向上和向下偏好不一致熵分类后的熵值小，而且更接近原始决策的偏好不一致熵，这说明基于全局偏好不一致熵的分类比其他两种情况的分类效果好。分类偏离度最小低至0.1282，这说明分类的结果比较接近原始决策。

关键词: 有序决策, 偏好不一致熵, 分类, 偏好关系

Abstract: Aiming at the problem of preference decision in multi-rule ordered decision-making system, according to the preference inconsistency of ordered decision-making, a preference decision-making method based on preference inconsistent entropy was proposed. Firstly, the Preference Inconsistence Entropy of Object (PIEO) was defined and used to measure the degree of preference inconsistency for a particular sample relative to the sample set. Then, according to that different attributes have different importances to the preference decision, a weighted Preference Inconsistence-based Entropy of Object (wPIEO) was proposed. Moreover, combining wPIEO with attribute preference inconsistency entropy in measuring attribute importance, a weighting method based on attribute preference inconsistent entropy was proposed. Finally, a preference decision algorithm based on sample preference inconsistent entropy was proposed. Two data sets, Pasture Production and Squalsh, were used to simulate the experiment. After the global Preference Inconsistent Entropy (gPIE) classification, the preference inconsistent entropy of each attribute was generally smaller than the entropy value based on the preference inconsistent entropy classification based on the up and down preferences, and it was closer to the preference inconsistent entropy of the original decision, which indicates that the classification based on gPIE was better than the other two cases. The classification deviation was as low as 0.1282, indicating that the classification results are close to the original decision.

Key words: ordered decision-making, preference inconsistence-based entropy, classification, preference relation

中图分类号:

TP18

潘伟, 佘堃. 基于偏好不一致熵的有序决策[J]. 计算机应用, 2017, 37(3): 796-800.

PAN Wei, SHE Kun. Ordered decision-making based on preference inconsistence-based entropy[J]. Journal of Computer Applications, 2017, 37(3): 796-800.

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基于偏好不一致熵的有序决策

Ordered decision-making based on preference inconsistence-based entropy

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