Attribute reduction in generalized multi-granularity formal decision contexts

WANG Pan; WANG Xinyi; LI Jinhai

doi:10.16152/j.cnki.xdxbzr.2026-03-011

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Attribute reduction in generalized multi-granularity formal decision contexts

更新时间：2026-07-07

- Attribute reduction in generalized multi-granularity formal decision contexts
- Journal of Northwest University (Natural Science Edition) Vol. 56, Issue 3, Pages: 583-595(2026)
- 作者机构：
  
  1.昆明理工大学　信息工程与自动化学院，云南　昆明　650500
  2.昆明理工大学　理学院，云南　昆明　650500
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2026-03-011
  CLC： O29;TP18
- Received：15 January 2026，
  
  Revised：2026-01-30，
  
  Published：25 June 2026
- 稿件说明：
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WANG Pan, WANG Xinyi, LI Jinhai. Attribute reduction in generalized multi-granularity formal decision contexts[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 583-595.
DOI：

WANG Pan, WANG Xinyi, LI Jinhai. Attribute reduction in generalized multi-granularity formal decision contexts[J]. Journal of Northwest University (Natural Science Edition), 2026, 56(3): 583-595. DOI： 10.16152/j.cnki.xdxbzr.2026-03-011.

摘要

目前存在的大部分决策形式背景属性约简方法只针对单粒度决策属性进行研究，较少考虑多粒度决策属性的情况。针对该问题，给出了广义多粒度决策形式背景属性约简方法，以实现信息系统属性约简，即先对多粒度属性进行粒度选择，再对冗余类属性块进行删除。为了刻画冗余类属性块，在广义多粒度决策形式背景中引入条件信息熵，以区分属性对于信息系统的重要程度，继而提出属性约简方法及相应实现算法。实验部分先对数据采用本文方法进行约简，再对约简后的数据进行分类预测，并将其与其他算法的分类精度进行对比，所得结果验证了本文提出的属性约简方法的有效性。

Abstract

Currently

most of the methods for attribute reduction in formal decision contexts only consider single-granularity decision attributes

rarely investigating multi-granularity decision attributes. Motivated by this

this paper presents a method for attribute reduction in generalized multi-granularity formal decision contexts to achieve attribute reduction in information systems. Specifically

it first selects granularity for multi-granularity attributes and then removes redundant class attribute blocks. In order to characterize redundant class attribute blocks

this paper introduces conditional information entropy in generalized multi-granularity formal decision contexts

thereby distinguishing the importance of attributes to the information system

and subsequently proposes an attribute reduction method as well as its corresponding implementation algorithm. In the experiments

the data are first reduced using our method

and then the reduced data are used for achieving classification tasks. Our method is further compared with other existing algorithms in terms of the classification accuracy. The obtained results validate the effectiveness of the proposed attribute reduction method.

关键词

Keywords

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