[1]程龙,钱文彬,王映龙,等.弱标记不完备决策系统的增量式属性约简算法[J].智能系统学报,2020,15(6):1079-1090.[doi:10.11992/tis.202001017]
CHENG Long,QIAN Wenbin,WANG Yinglong,et al.An incremental attribute reduction algorithm for incomplete decision system with weak labeling[J].CAAI Transactions on Intelligent Systems,2020,15(6):1079-1090.[doi:10.11992/tis.202001017]
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CHENG Long,QIAN Wenbin,WANG Yinglong,et al.An incremental attribute reduction algorithm for incomplete decision system with weak labeling[J].CAAI Transactions on Intelligent Systems,2020,15(6):1079-1090.[doi:10.11992/tis.202001017]
弱标记不完备决策系统的增量式属性约简算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第6期
页码:
1079-1090
栏目:
学术论文—知识工程
出版日期:
2020-11-05
- Title:
- An incremental attribute reduction algorithm for incomplete decision system with weak labeling
- Keywords:
- attribute reduction; rough set; discernibility pair; mixed data; incremental learning; semi-supervised learning; relative importance; dynamic data
- 分类号:
- TP391
- 摘要:
- 在许多现实应用领域中，由于数据标注代价昂贵，且数据往往呈现动态变化，因此存在大量弱标记的不完备数据。针对上述复杂应用场景，本文以粒计算理论为基础，从区分性视角给出不完备数据的区分对概念，同时给出属性相对重要度的度量方法，并设计面向弱标记不完备决策系统的属性约简算法。该算法能在迭代过程中不断缩减搜索空间，提高属性约简效率；并根据实例的动态变化情况，分析属性约简的动态更新机制；在此基础上，设计了半监督条件下的增量式属性约简算法。最后，通过实验验证了算法的可行性和有效性。
- Abstract:
- Due to the high cost of data annotation and dynamic change of data, many practical applications have a lot of incomplete data with weak labeling. In view of the above complex scenarios, based on the theory of granular computing, the concept of discernibility pairs of incomplete data is proposed and provides a measurement method for the relative importance of attributes. The attribute reduction algorithm is designed for an incomplete decision system with weak labeling, which can reduce the search space and improve the efficiency of attribute reduction. Besides, the dynamic updating mechanism of attribute reduction is analyzed based on the dynamic change of instances. In this study, an incremental attribute reduction algorithm is designed under a semi-supervised scene, and the experimental results show the feasibility and effectiveness of the proposed algorithm.
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备注/Memo
收稿日期:2020-01-09。
基金项目:国家自然科学基金项目(61966016)；江西省自然科学基金项目(20192BAB207018)；江西省教育厅科学技术研究项目(GJJ180200)
作者简介:程龙，硕士研究生，主要研究方向为粒计算与知识发现;钱文彬，副教授，博士，主要研究方向为粒计算、知识发现与机器学习。主持国家自然科学基金项目2项，江西省自然科学基金项目2项。发表学术论文30余篇;王映龙，教授，博士，主要研究方向为知识发现与数据挖掘。参与国家自然科学基金项目2项，主持江西省自然科学基金项目3项。发表学术论文20余篇
通讯作者:钱文彬.E-mail:qianwenbin1027@126.com
更新日期/Last Update:
2020-12-25