[1]刘牧雷,徐菲菲.基于三支决策的序列数据代价敏感分类算法[J].智能系统学报,2019,14(06):1255-1261.[doi:10.11992/tis.201905049]
 LIU Mulei,XU Feifei.A sequence data, cost-sensitive classification algorithm based on three-way decisions[J].CAAI Transactions on Intelligent Systems,2019,14(06):1255-1261.[doi:10.11992/tis.201905049]
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基于三支决策的序列数据代价敏感分类算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1255-1261
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
A sequence data, cost-sensitive classification algorithm based on three-way decisions
作者:
刘牧雷 徐菲菲
上海电力学院 计算机科学与技术学院, 上海 200090
Author(s):
LIU Mulei XU Feifei
School of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090, China
关键词:
代价敏感三支决策长短期记忆网络序列数据分类分类算法高代价类别代价评估
Keywords:
cost-sensitivethree-way decisionLSTMsequence data classificationclassification algorithmhigh-cost categoriecost estimate
分类号:
TP181
DOI:
10.11992/tis.201905049
摘要:
代价敏感分类区别于一般分类方法,更关注高代价类别的分类准确性而容忍全局分类的准确性。三支决策作为一种代价敏感分类问题的解决思路,缺乏对序列数据的支持。结合LSTM模型处理序列数据的能力,提出一种使用三支决策(3WD)改进的序列数据分类方法。方法经过LSTM网络对原数据进行粗分类;对分类结果进行整体代价评估;最终,对高风险分类进行延迟或拒绝处理。方法在4个数据集上进行了测试,并进行了2组对比实验。实验结果表明:本文方法在不改变LSTM模型的情况下,对LSTM模型的分类结果进行了代价区分。
Abstract:
Cost-sensitive classification is different from the general classification method, which pays more attention to the classification accuracy of high-cost categories, but tolerates the accuracy of global classification. Three-way decisions are a solution to a cost-sensitive classification problem and lack support for sequence data. Combined with the ability of the LSTM model in sequence data processing, a method for classifying sequence data a using three-way decision method (3WD) is proposed. First, a general classification of the original data was done through the LSTM network; second, an overall cost estimate was performed on the classification result of step one; finally, the high-risk result was delayed or rejected. Methods were tested on four data sets and two sets of comparative experiments were performed. Experimental results showed that the new method distinguished the classification results of the LSTM model without changing the original structure.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-05-26。
作者简介:刘牧雷,男,1993年生,硕士研究生,主要研究方向为三支决策、代价敏感分类;徐菲菲,女,1983年生,副教授,中国计算机学会和中国人工智能学会会员,主要研究方向为粒计算理论、粗糙集理论、数据挖掘、人工智能与机器学习。主持国家自然科学基金项目1项;上海市教育发展基金会和上海市教育委员会"晨光计划"1项、上海市教育委员会科研创新项目1项等。
通讯作者:徐菲菲.E-mail:xufeifei1983@hotmail.com
更新日期/Last Update: 2019-12-25