基于改进变精度贝叶斯粗糙集的感性知识获取

doi:10.12068/j.issn.1005-3026.2018.12.024

东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (12): 1794-1799.DOI: 10.12068/j.issn.1005-3026.2018.12.024

基于改进变精度贝叶斯粗糙集的感性知识获取

胡名彩，郭伏，叶国全

(东北大学工商管理学院，辽宁沈阳110169)

收稿日期:2017-09-11 修回日期:2017-09-11 出版日期:2018-12-15 发布日期:2018-12-19
通讯作者: 胡名彩
作者简介:胡名彩(1992-)，男，江西吉安人，东北大学博士研究生; 郭伏(1964-)，女，辽宁开原人，东北大学教授，博士生导师.冯明杰(1971-)，男，河南禹州人，东北大学副教授; 王恩刚(1962-)，男，辽宁沈阳人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(71471033， 71771045)；东北大学“双一流”学科建设资助项目(02050021940101).国家自然科学基金资助项目(51171041).

Kansei Knowledge Acquisition Based on the Improved Variable Precision Bayesian Rough Set

HU Ming-cai， GUO Fu， YE Guo-quan

School of Business Administration， Northeastern University， Shenyang 110169， China.

Received:2017-09-11 Revised:2017-09-11 Online:2018-12-15 Published:2018-12-19
Contact: GUO Fu
About author:-
Supported by:
-

摘要/Abstract

摘要： 变精度贝叶斯粗糙集方法是感性知识获取中处理用户群个性化感性差异的一种柔性方法，针对其在感性规则提取阶段可能产生的组合爆炸问题，提出了一种基于顺序覆盖策略的改进算法.该算法以感性决策类的近似区域作为输入，以选取覆盖能力最大的合取项为贪心搜索策略实现规则特化.在此基础上，通过迭代学习逐步完成对近似区域的覆盖和决策规则集的提取.最后，通过基础实例和烤面包机外观设计实例验证了改进方法的有效性.

关键词: 感性工学, 知识获取, 决策规则, 贝叶斯粗糙集, 顺序覆盖策略

Abstract: The variable precision Bayesian rough set(VPBRS)approach is a flexible method for Kansei knowledge acquisition to accommodate the individual differences within a user group. In order to handle the possible combinatorial explosion at the stage of Kansei rule extraction， an improved algorithm based on sequential covering strategy is proposed. Basically， the approximation regions of Kansei decision classes are taken as the input， and the selection of conjunctive items with maximum covering ability is taken as the greedy search strategy for rule specialization. On this basis， the approximation region is covered step by step through iterative learning， and the decision rule set is extracted. A basic example and a design example of toaster appearance are conducted， whose results show that the improved VPBRS approach is effective.

Key words: Kansei engineering, knowledge acquisition, decision rule, Bayesian rough sets, sequential covering strategy

中图分类号:

TB472

胡名彩，郭伏，叶国全. 基于改进变精度贝叶斯粗糙集的感性知识获取[J]. 东北大学学报:自然科学版, 2018, 39(12): 1794-1799.

HU Ming-cai， GUO Fu， YE Guo-quan. Kansei Knowledge Acquisition Based on the Improved Variable Precision Bayesian Rough Set[J]. Journal of Northeastern University Natural Science, 2018, 39(12): 1794-1799.

参考文献

[1]Meiselman H L.A review of the current state of emotion research in product development［J］.Food Research International，2015，76:192-199.
[2]Nagamachi M.Kansei engineering:a new ergonomic consumer-oriented technology for product development［J］.International Journal of Industrial Ergonomics，1995，15(1):3-11.
[3]Vieira J，Osório J M A，Mouta S，et al.Kansei engineering as a tool for the design of in-vehicle rubber keypads［J］.Applied Ergonomics，2017，61:1-11.
[4]赵晓煜，曹忠鹏，张昊.基于感性工学的服务场景设计方法［J］.东北大学学报(自然科学版)，2011，32(9):1360-1363.(Zhao Xiao-yu，Cao Zhong-peng，Zhang Hao.Method for designing servicescape based on Kansei engineering［J］.Journal of Northeastern University(Natural Science)，2011，32(9):1360-1363.)
[5]Shieh M D，Yeh Y E，Huang C L.Eliciting design knowledge from affective responses using rough sets and Kansei engineering system［J］.Journal of Ambient Intelligence and Humanized Computing，2016，7(1):107-120.
[6]Hong S W，Han S H，Kim K J.Optimal balancing of multiple affective satisfaction dimensions:a case study on mobile phones［J］.International Journal of Industrial Ergonomics，2008，38(3/4):272-279.
[7]Yan H B，Ma T，Sriboonchitta S，et al.A stochastic dominance based approach to consumer-oriented Kansei evaluation with multiple priorities［J］.Annals of Operations Research，2017，256(2):329-357.
[8]Jokinen J P.Emotional user experience:traits，events，and states［J］.International Journal of Human-Computer Studies，2015，76:67-77.
[9]Li Y，Zhu L.Optimisation of product form design using fuzzy integral-based Taguchi method［J］.Journal of Engineering Design，2017，28(7/8/9):480-504.
[10]Diego-Mas J A，Alcaide-Marzal J.Single users′ affective responses models for product form design［J］.International Journal of Industrial Ergonomics，2016，53:102-114.
[11]Nishino T，Nagamachi M，Tanaka H.Variable precision Bayesian rough set model and its application to human evaluation data［C］// Rough Sets，Fuzzy Sets，Data Mining，and Granular Computing.Berlin:Springer-Verlag，2005:294-303.
[12]Nishino T.Rough set theory and Kansei/affective engineering［M］// Kansei/Affective engineering.Boca Raton:CRC Press，2010:207-226.
[13]Baszczyński J，Sowiński R，Szelag M.Sequential covering rule induction algorithm for variable consistency rough set approaches［J］.Information Sciences，2011，181(5):987-1002.
[14]Grzymala-Busse J W.LERS:A system for learning from examples based on rough sets［M］// Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory.Berlin:Springer，1992:3-18.
[15]Chang Y M，Chen C W.Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design［J］.International Journal of Industrial Ergonomics，2016，56:97-105.

基于改进变精度贝叶斯粗糙集的感性知识获取

Kansei Knowledge Acquisition Based on the Improved Variable Precision Bayesian Rough Set

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

编辑推荐

Metrics

本文评价

[1]	闫佳祺，贾建锋，赵若男，连粤丰. 高承诺型人力资源管理实践对突破式创新的影响[J]. 东北大学学报（自然科学版）, 2022, 43(8): 1209-1216.
[2]	赵晓煜;曹忠鹏;张昊;. 基于感性工学的服务场景设计方法[J]. 东北大学学报(自然科学版), 2011, 32(9): 1360-1363.
[3]	赵珍;胡学发;何大阔;王福利;. 水压试验机故障诊断专家系统的知识获取[J]. 东北大学学报(自然科学版), 2008, 29(12): 1677-1680.
[4]	郭薇;魏颖;周翰逊;薛定宇;. 基于混合分类的肺结节检测算法[J]. 东北大学学报(自然科学版), 2008, 29(11): 1528-1531.
[5]	孙杰;李晶皎;张俐;姚天顺. 机器翻译系统中词类搭配规则的自动获取方法[J]. 东北大学学报(自然科学版), 1999, 20(2): 4--.