采用邻域决策分辨率的特征选择算法

诸文智; 司刚全; 张彦斌

doi:10.7652/xjtuxb201302004

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采用邻域决策分辨率的特征选择算法

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- 采用邻域决策分辨率的特征选择算法
- Feature Selection Algorithm Based on Neighborhood Decision Distinguishing Rate
- 西安交通大学学报 2013年47卷第2期页码：20-27
- 作者机构：
  
  西安交通大学电气工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2012-06-16。作者简介: 诸文智(1982—),男,博士生;张彦斌(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61005058);教育部高等学校博士学科点专项科研基金资助项目(20090201110019,20070698059)
- DOI：10.7652/xjtuxb201302004
  中图分类号： TP274
- 网络首发：2013-02-10，
  
  纸质出版：2013
- 稿件说明：
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诸文智, 司刚全, 张彦斌. 采用邻域决策分辨率的特征选择算法[J]. 西安交通大学学报, 2013,47(2):20-27.

Feature Selection Algorithm Based on Neighborhood Decision Distinguishing Rate[J]. 2013, 47(2): 20-27.
诸文智, 司刚全, 张彦斌. 采用邻域决策分辨率的特征选择算法[J]. 西安交通大学学报, 2013,47(2):20-27. DOI： 10.7652/xjtuxb201302004.

Feature Selection Algorithm Based on Neighborhood Decision Distinguishing Rate[J]. 2013, 47(2): 20-27. DOI： 10.7652/xjtuxb201302004.

摘要

针对目前基于粗糙集模型的特征选择算法无法直接应用于数值型数据、必须经过离散化过程而造成决策信息丢失的问题

提出了一种基于邻域决策分辨率的特征选择算法。该算法根据邻域信息粒中决策分布与其分类能力间的关系

提出了邻域决策确定性(N

)来衡量单个信息粒的决策分辨能力; 并根据特征向量空间上所有信息粒所具有的N

累加值

定义了邻域决策分辨率作为特征子集上决策可分辨性的量度

从而将名义型和数值型数据统一在同一特征选择算法框架下。仿真实验和实际应用的结果表明

该算法性能优于目前主流基于邻域粗糙集的特征选择方法。

Abstract

The current feature selection algorithms based on the neighborhood rough set(NRS)model are unable to evaluate numerical dataset directly

a discretization procedure becomes necessary to transform the datasets into discrete forms

but inevitably leads to useful decision information loss. To solve this difficulty

a feature selection algorithm based on the neighborhood effective information rate is proposed. In view point of granulated neighborhood

the relation between the decision discernibility and the decision distribution is analyzed

and the neighborhood decision certainty(N

)is defined to indicate the degree of distinguishing capability in each individual neighborhood granule. The neighborhood decision distinguishing rate(NDDR)of the feature subset

which evaluates the ability of the subspace to approximate decision space

is est

ablished based on the sum of the N

values of the information granules induced by the corresponding feature space. Then the nominal and numerical datasets can be integrated into the same feature selection algorithm framework. The simulation and application illustrate that the proposed algorithm outperforms the other NRS-based ones.

关键词

Keywords

references

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