[1]杨成东,邓廷权.综合属性选择和删除的属性约简方法[J].智能系统学报,2013,8(2):183-186.[doi:10.3969/j.issn.1673-4785.201209056]
YANG Chengdong,DENG Tingquan.An approach to attribute reduction combining attribute selection and deletion[J].CAAI Transactions on Intelligent Systems,2013,8(2):183-186.[doi:10.3969/j.issn.1673-4785.201209056]
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YANG Chengdong,DENG Tingquan.An approach to attribute reduction combining attribute selection and deletion[J].CAAI Transactions on Intelligent Systems,2013,8(2):183-186.[doi:10.3969/j.issn.1673-4785.201209056]
综合属性选择和删除的属性约简方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第2期
页码:
183-186
栏目:
学术论文—人工智能基础
出版日期:
2013-04-25
- Title:
- An approach to attribute reduction combining attribute selection and deletion
- 文章编号:
- 1673-4785(2013)02-0183-04
- Keywords:
- discernibility matrix; attribute reduction; information redundancy; artificial intelligence; machine learning; attribute selection; attribute deletion
- 分类号:
- TP301.6
- 文献标志码:
- A
- 摘要:
- 属性约简能有效地消除信息冗余，广泛应用于人工智能、机器学习.通过实例指出基于辨识矩阵的经典的属性约简方法存在不能得到约简的可能性，仍具有冗余性.因此，提出了综合属性选择和删除算法的辨识矩阵属性约简方法，并有效解决该问题.通过UCI标准数据集验证表明，新方法比经典方法进一步减少了属性的个数，凸显其实用性和有效性.
- Abstract:
- Attribute reduction has been defined as a method for removing information redundancy effectively, which has been widely applied to artificial intelligence, and machine learning. However, an example demonstrates classical attribute reduction approaches based on discernibility matrix may not get a reduction with redundancy. Therefore, an attribute reduction based on discernibility matrix combining attribute selection and deletion was proposed and thus, the problem was solved effectively. Moreover, UCI standard data sets provide further explanations on the feasibility, effectiveness, and as well as additional information on reducing the number of attributes without the classical approaches.
- 参考文献/References:
-
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备注/Memo
收稿日期：2012-09-25.
网络出版日期：2013-04-09.
基金项目：山东省高等学校科技计划资助项目（J12LN91）; 山东省信息化与工业化融合专项课题资助项目（2012EI100）.
通信作者：杨成东.
E-mail: yangchengdong@lyu.edu.cn.
作者简介：
杨成东，男，1984年生，讲师，博士，主要研究方向为数据挖掘、粗糙集理论、智能计算.主持山东省高等学校科技计划项目等，发表学术论文十余篇.
?邓廷权，男，1965年生，教授，博士生导师，主要研究方向为模糊信息分析、数学形态学与图像分析、智能识别与计算机视觉.主持国家自然科学基金、中国博士后科学基金、黑龙江省博士后科学基金等多项科研项目.近年来，发表学术论文30余篇，其中半数被SCI、EI、ISPT等检索.
更新日期/Last Update:
2013-05-26