[1]高学义,张楠,童向荣,等.广义分布保持属性约简研究[J].智能系统学报,2017,12(3):377-385.[doi:10.11992/tis.201704025]
GAO Xueyi,ZHANG Nan,TONG Xiangrong,et al.Research on attribute reduction using generalized distribution preservation[J].CAAI Transactions on Intelligent Systems,2017,12(3):377-385.[doi:10.11992/tis.201704025]
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GAO Xueyi,ZHANG Nan,TONG Xiangrong,et al.Research on attribute reduction using generalized distribution preservation[J].CAAI Transactions on Intelligent Systems,2017,12(3):377-385.[doi:10.11992/tis.201704025]
广义分布保持属性约简研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第3期
页码:
377-385
栏目:
学术论文—人工智能基础
出版日期:
2017-06-25
- Title:
- Research on attribute reduction using generalized distribution preservation
- Keywords:
- distribution preservation; attribute reduction; rough sets; probability distribution; discernibility matrix
- 分类号:
- TP181
- 摘要:
- 属性约简是粗糙集理论的重要研究内容之一。分布约简保证约简前后每个对象的概率分布保持不变，即保证每条规则的置信度在约简前后不发生改变。实际应用中，人们往往更加关注可信度较高或较低的规则。因此，在本文中引入了广义分布保持属性约简，该属性约简可以保证规则的置信度P（P∈[0，α]或[β，1]）在约简前后不变。同时，给出了广义分布保持属性约简的判定方法与基于差别矩阵的广义分布保持属性约简算法，深入讨论了几种特殊情形下的广义分布保持约简。最后，在4个UCI数据集上进行的实验分析表明，几种特殊情形下的广义分布保持属性约简可退化为已有的一些属性约简，且在不同置信区间下求得的广义分布保持属性约简存在包含关系，验证了相关结论的正确性。
- Abstract:
- Attribute reduction is a pertinent issue in rough set theory. Distribution reduction ensures that the probability distribution of each target does not change before and after reduction; i.e., it ensures that the confidence of every rule remains unchanged before and after reduction. In actual applications, people are often interested in rules that have higher or lower confidences. Thus, attribute reduction based on generalized distribution preservation is proposed in this paper. Confidences in [0, α] or [β, 1] were unchanged using the proposed technique. We also propose judgment methods for generalized-distribution-preservation attribute reduction and investigate the generalized attribute-reduction algorithm based on a discernibility matrix. Some special cases with respect to generalized-distribution-preservation attribute reduction are discussed in depth. Finally, experiments on four data sets downloaded from UCI show that some special cases with respect to generalized distribution preservation reduction could degenerate into some existing attribute reductions and inclusion relations exist in generalized distribution preservation attribute reduction under different confidence intervals, verifying the correctness of the relevant conclusions.
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备注/Memo
收稿日期:2017-04-19。
基金项目:国家自然科学基金项目（61403329，61572418，61502410，61572419）；山东省自然科学基金项目（ZR2013FQ020，ZR2015PF 010）；山东省高等学校科技计划项目（J15LN09，116LN17）.
作者简介:高学义,男,1992年生,硕士研究生,主要研究方向为粗糙集、数据挖掘与机器学习;张楠,男,1979年生,博士,主要研究方向为粗糙集、认知信息学与人工智能;童向荣,男,1975年生,教授,博士,主要研究方向为多Agent系统、分布式人工智能与数据挖掘技术。
通讯作者:张楠.E-mail:zhangnan0851@163.com.
更新日期/Last Update:
2017-06-25