计算机科学 ›› 2018, Vol. 45 ›› Issue (10): 6-10.doi: 10.11896/j.issn.1002-137X.2018.10.002

• 2018 年中国粒计算与知识发现学术会议 • 上一篇    下一篇

代价敏感的序贯三支决策方法

邢颖1, 李德玉1,2, 王素格1,2   

  1. 山西大学计算机与信息技术学院 太原030006 1
    山西大学计算智能与中文信息处理教育部重点实验室 太原030006 2
  • 收稿日期:2018-03-09 出版日期:2018-11-05 发布日期:2018-11-05
  • 作者简介:邢 颖(1992-),女,硕士生,主要研究方向为粗糙集、数据挖掘,E-mail:715661871@qq.com;李德玉(1965-),男,博士,教授,主要研究方向为粒计算、机器学习,E-mail:lidy@sxu.edu.cn(通信作者);王素格(1964-),女,博士,教授,主要研究方向为自然语言处理、文本挖掘,E-mail:wsg@sxu.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(61672331,61632011,61573231,61432011,61603229),山西省自然科学基金项目(201601D021076)资助。

Cost-sensitive Sequential Three-way Decision Making Method

XING Ying1, LI De-yu1,2, WANG Su-ge1,2   

  1. School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China 1
    Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University,Taiyuan 030006,China 2
  • Received:2018-03-09 Online:2018-11-05 Published:2018-11-05

摘要: 在现实决策中,代价敏感问题是影响人类决策的重要因素之一,许多研究者致力于降低决策的代价。现阶段,在粗糙集领域中,研究者多基于DTRS模型且仅考虑某一种代价,不够全面。针对以上问题,利用序贯三支决策模型对两种代价的敏感性,通过多层次粒结构可以有效降低决策总代价,且能够更好地模拟人类动态渐进的决策过程。在序贯三支决策模型的基础上,构造了多层次粒结构;将各个属性的测试代价与其分类能力相关联,从信息熵的角度为其设置测试代价;与此同时,将属性约简与序贯三支决策相结合,利用基于代价最小准则的属性约简去除冗余属性及不相关属性对代价的影响。在7个UCI数据集上的实验结果显示,在保证较高准确度的同时,决策的总代价平均下降了26%左右,充分验证了该方法的有效性。

关键词: 代价敏感, 多粒度, 序贯三支决策, 属性约简

Abstract: In realistic decision-making,cost-sensitive issue is one of the important factors which affects human decision-making,and many researchers are committed to reducing the cost of decision-making.At present,in the field of rough set,many researchers mainly research decision-making based on DTRS model and only consider a certain cost,which is not comprehensive enough.While sequential three-way decision model is sensitive to two kinds of costs,and the multi-level granular structure can effectively reduce the total cost of decision and can better simulate the process of human’s dynamic and gradual decision-making.Based on sequential three decision models,this paper constructed a multi-level granular structure.It relates the test cost of each attribute to its classification ability and sets the test cost from the perspective of information entropy.At the same time,combined with the sequential three decisions,the attribute reduction based on the minimum cost criterion is used to remove the influence of redundant attributes and irrelevant attributes on the cost.The experimental results on the seven UCI datasets show that while high accuracy is ensured ,the total cost of decision-making is dropped by an average of 26%,which fully validates the effectiveness of the proposed method.

Key words: Attribute reduction, Cost-sensitive, Multi-granularity, Sequential three-way decision

中图分类号: 

  • TP181
[1]LI H X,ZHOU X Z,HUANG B,et al.Cost-sensitive three-way decision:a sequential strategy[C]∥International Conference on Rough Sets and Knowledge Technology.Berlin:Springer,2013:325-337.
[2]ZHANG Y P,ZOU H J,CHEN X,et al.Cost-sensitive three-way decisions model based on CCA[C]∥International Confe-rence on Rough Sets and Current Trends in Computing.Switzerland:Springer,2014,8536:172-180.
[3]JIAO X Y,TANG Z M,LIAO W H,et al.Minimum cost attri- bute reduction in decision-theoretic rough set models[J].Information Sciences,2013,219(1):151-167.
[4]YANG X B,QI Y S,SONG X N,et al.Test cost sensitive multigranulation rough set:model and minimal cost selection[J].Information Sciences,2013,250(11):184-199.
[5]LI H X,ZHOU X Z,ZHAO J B,et al.Cost-sensitive classification based on decision-theoretic rough set model[C]∥International Conference on Rough Sets and Knowledge Technology.Springer-Verlag,2012:379-388.
[6]JU H R,LI H X,YANG X B,et al.Cost-sensitive rough set:a multi-granulation approach[J].Knowledge-Based Systems,2017,123(9):137-153.
[7]DOU H L,YANG X B,SONG X N,et al.Decision-theoretic rough set:a multicost strategy[J].Knowledge-Based Systems,2016,91(1):71-83.
[8]JU H R,YANG X B,YU H L,et al.Cost-sensitive rough set approach[J].Information Sciences,2016,355-356(C):282-298.
[9]MIN F,LIU Q H.A hierarchical model for test-cost-sensitive decision systems[J].Information Sciences,2009,179(14):2442-2452.
[10]YAO Y Y,WONG S K M,LINGRAS P.A decision-theoretic rough set model[C]∥Proceedings of ISMIS.1990:17-25.
[11]YAO Y Y,WONG S K M.A decision theoretic framework for approximating concepts[J].International Journal of Man-Machine Studies,1992,37(6):793-809.
[12]YAO Y Y.The superiority of three-way decisions in probabilistic rough set models[J].Information Sciences,2011,181(6):1080-1096.
[13]JIA X Y,LIAO W H,TANG Z M,et al.Minimum cost attribute reduction in decision-theoretic rough set models[J].Information Sciences,2013,219(1):151-167.
[14]YAO Y Y.An outline of a theory of three-way decisions[C]∥International Conference on Rough Sets and Current Trends in Computing.Springer,2012:1-17.
[15]YAO Y Y.Three-way decisions with probabilistic rough sets[J].Information Sciences,2010,180(3):341-353.
[16]YAO Y Y.The superiority of three-way decisions in probabilistic rough set models[J].Information Sciences,2011,181(6):1080-1096.
[17]YAO Y Y.Probabilistic approaches to rough sets[J].Expert Systems,2003,20(5):287-297.
[18]YAO Y Y.Three-way decision:an interpretation of rules in rough set theory[C]∥International Conference on Rough Sets and Knowledge Technology.Springer-Verlag,2009:642-649.
[19]YAO Y Y,DENG X F.Sequential three-way decisions with probabilistic rough sets[J].Information Sciences,2010,180(3):341-353.
[20]YAO Y Y.Granular computing and sequential three-way decisions[C]∥International Conference on Rough Sets and Know-ledge Technology.Berlin:Springer,2013,8171:16-27.
[21]刘盾,李天瑞,苗夺谦,等.三支决策与粒计算[M].北京:科学出版社,2013:43-47.
[22]YANG X B,LI T R,FUJITA H,et al.A unified model of sequential three-way decisions and multilevel incremental proces-sing[J].Knowledge-Based Systems,2017,134(20):172-188.
[1] 秦琪琦, 张月琴, 王润泽, 张泽华.
基于知识图谱的层次粒化推荐方法
Hierarchical Granulation Recommendation Method Based on Knowledge Graph
计算机科学, 2022, 49(8): 64-69. https://doi.org/10.11896/jsjkx.210600111
[2] 张源, 康乐, 宫朝辉, 张志鸿.
基于Bi-LSTM的期货市场关联交易行为检测方法
Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM
计算机科学, 2022, 49(7): 31-39. https://doi.org/10.11896/jsjkx.210400304
[3] 杨斐斐, 沈思妤, 申德荣, 聂铁铮, 寇月.
面向数据融合的多粒度数据溯源方法
Method on Multi-granularity Data Provenance for Data Fusion
计算机科学, 2022, 49(5): 120-128. https://doi.org/10.11896/jsjkx.210300092
[4] 李京泰, 王晓丹.
基于代价敏感激活函数XGBoost的不平衡数据分类方法
XGBoost for Imbalanced Data Based on Cost-sensitive Activation Function
计算机科学, 2022, 49(5): 135-143. https://doi.org/10.11896/jsjkx.210400064
[5] 王子茵, 李磊军, 米据生, 李美争, 解滨.
基于误分代价的变精度模糊粗糙集属性约简
Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost
计算机科学, 2022, 49(4): 161-167. https://doi.org/10.11896/jsjkx.210500211
[6] 王志成, 高灿, 邢金明.
一种基于正域的三支近似约简
Three-way Approximate Reduction Based on Positive Region
计算机科学, 2022, 49(4): 168-173. https://doi.org/10.11896/jsjkx.210500067
[7] 胡艳丽, 童谭骞, 张啸宇, 彭娟.
融入自注意力机制的深度学习情感分析方法
Self-attention-based BGRU and CNN for Sentiment Analysis
计算机科学, 2022, 49(1): 252-258. https://doi.org/10.11896/jsjkx.210600063
[8] 黄颖琦, 陈红梅.
基于代价敏感卷积神经网络的非平衡问题混合方法
Cost-sensitive Convolutional Neural Network Based Hybrid Method for Imbalanced Data Classification
计算机科学, 2021, 48(9): 77-85. https://doi.org/10.11896/jsjkx.200900013
[9] 王栋, 周大可, 黄有达, 杨欣.
基于多尺度多粒度特征的行人重识别
Multi-scale Multi-granularity Feature for Pedestrian Re-identification
计算机科学, 2021, 48(7): 238-244. https://doi.org/10.11896/jsjkx.200600043
[10] 李艳, 范斌, 郭劼, 林梓源, 赵曌.
基于k-原型聚类和粗糙集的属性约简方法
Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets
计算机科学, 2021, 48(6A): 342-348. https://doi.org/10.11896/jsjkx.201000053
[11] 王政, 姜春茂.
一种基于三支决策的云任务调度优化算法
Cloud Task Scheduling Algorithm Based on Three-way Decisions
计算机科学, 2021, 48(6A): 420-426. https://doi.org/10.11896/jsjkx.201000023
[12] 吕乐宾, 刘群, 彭露, 邓维斌, 王崇宇.
结合多粒度信息的文本匹配融合模型
Text Matching Fusion Model Combining Multi-granularity Information
计算机科学, 2021, 48(6): 196-201. https://doi.org/10.11896/jsjkx.200700100
[13] 丁玲, 向阳.
基于分层次多粒度语义融合的中文事件检测
Chinese Event Detection with Hierarchical and Multi-granularity Semantic Fusion
计算机科学, 2021, 48(5): 202-208. https://doi.org/10.11896/jsjkx.200800038
[14] 周晓进, 徐陈铭, 阮彤.
面向中文电子病历的多粒度医疗实体识别
Multi-granularity Medical Entity Recognition for Chinese Electronic Medical Records
计算机科学, 2021, 48(4): 237-242. https://doi.org/10.11896/jsjkx.200100036
[15] 陈卓, 王国胤, 刘群.
结合多粒度特征融合的自然场景文本检测方法
Natural Scene Text Detection Algorithm Combining Multi-granularity Feature Fusion
计算机科学, 2021, 48(12): 243-248. https://doi.org/10.11896/jsjkx.201000154
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!