[1]赵天娜,苗夺谦,米据生,等.面向混合数据的多伴随三支决策[J].智能系统学报,2019,14(6):1092-1099.[doi:10.11992/tis.201905048]
ZHAO Tianna,MIAO Duoqian,MI Jusheng,et al.Multi-adjoint three-way decisions on heterogeneous data[J].CAAI Transactions on Intelligent Systems,2019,14(6):1092-1099.[doi:10.11992/tis.201905048]
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ZHAO Tianna,MIAO Duoqian,MI Jusheng,et al.Multi-adjoint three-way decisions on heterogeneous data[J].CAAI Transactions on Intelligent Systems,2019,14(6):1092-1099.[doi:10.11992/tis.201905048]
面向混合数据的多伴随三支决策
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第6期
页码:
1092-1099
栏目:
学术论文—机器感知与模式识别
出版日期:
2019-11-05
- Title:
- Multi-adjoint three-way decisions on heterogeneous data
- Keywords:
- heterogeneous data; fuzzy rough set; three-way decisions; multi-adjoint; cost-sensitive; knowledge representation; classification
- 分类号:
- TP391
- 摘要:
- 针对混合数据的知识表示和分类的问题，在思考混合数据的有效表示时，提出代价敏感多伴随模糊粗糙集模型，在解决混合数据的分类问题上，引入三支决策思想，同时在多伴随模型基础上做了两点改进：1）提出贴近代价敏感多伴随模糊粗糙集模型特点的概率定义；2）借助双量化延迟代价目标函数的思想，构造面向混合数据的新型三支决策模型。该模型具有如下特点：1）引入多个伴随对，模拟了数值型属性和符号型属性之间异构互补的关系；2）定义多伴随算子，充分表达了不同类型属性之间的偏好；3）结合模糊粗糙集，克服了分类问题的不确定性；4）考虑获取不同类型属性的代价，提高了应用到实际生活的可能性。最后用实例验证了此模型的有效性。
- Abstract:
- Considering the problem of knowledge representation and classification relating to heterogeneous data, a cost-sensitive multi-adjoint fuzzy rough set model is proposed for the effective representation of heterogeneous data and in order to solve the classification problem of heterogeneous data, the idea of three-way decisions is introduced. Moreover, two improvements are made on the basis of the multi-adjoint model: 1) A revised probability definition is presented to approximately characterize the cost-sensitive fuzzy rough set model. 2) Based on the idea of the dual quantization delay cost objective function, a novel three-way decisions model is constructed for heterogeneous data. This model has the following characteristics: 1) Multiple adjoint pairs are introduced to simulate the relationship of heterogeneous complementarity between numerical attribute and categorical attribute. 2) The multi-adjoint operator is defined to fully express the preference among different attributes. 3) A fuzzy rough set is combined to overcome the uncertainty of the classification problem. 4) The cost of acquiring both numerical and categorical attributes is considered to improve the possibility of application to real life. The effectiveness of the model is verified in the heterogeneous dataset.
- 参考文献/References:
-
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备注/Memo
收稿日期:2019-05-24。
基金项目:国家重点研发项目（213）；国家自然科学基金项目（61673301，61573127，61763031）；河北省自然科学基金项目（A2018210120）；公安部重大专项项目（20170004）.
作者简介:赵天娜,女,1992年生,博士研究生,主要研究方向为模糊粗糙集、多伴随理论、人工智能、机器学习;苗夺谦,男,1964年生,教授,博士生导师,主要研究方向为人工智能、机器学习、大数据分析、粒度计算。主持完成国家自然科学基金项目6项,在研项目有国家重点研发计划课题和公安部重点计划项目。荣获CAAI吴文俊人工智能自然科学奖二等奖、国家教学成果二等奖,授权专利12项。出版教材和学术著作10部。发表学术论文100余篇;米据生,男,1966年生,教授,博士生导师,主要研究方向为粗糙集、粒计算、概念格、数据挖掘与近似推理。主持国家自然科学基金项目3项,教育部博士点基金项目1项。获得省级自然科学奖3项。发表学术论文130余篇
通讯作者:赵天娜.E-mail:1810375@tongji.edu.cn
更新日期/Last Update:
2019-12-25