An incremental attribute reduction algorithm for distributed decision information system

XU Yan-bai; JING Yun-ge

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An incremental attribute reduction algorithm for distributed decision information system

更新时间：2026-09-04

- An incremental attribute reduction algorithm for distributed decision information system
- Vol. 37, Issue 9, Pages: 31-36(2020)
- 作者机构：
  
  运城学院，数学与信息技术学院,山西,运城,044000
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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徐岩柏, 景运革. 分布决策信息系统增量属性约简算法[J]. 微电子学与计算机, 2020, 37(9): 31-36.

XU Yan-bai, JING Yun-ge. An incremental attribute reduction algorithm for distributed decision information system[J]. 2020, 37(9): 31-36.
徐岩柏, 景运革. 分布决策信息系统增量属性约简算法[J]. 微电子学与计算机, 2020, 37(9): 31-36. DOI：

XU Yan-bai, JING Yun-ge. An incremental attribute reduction algorithm for distributed decision information system[J]. 2020, 37(9): 31-36. DOI：

摘要

为了解决数据动态变化后如何快速更新属性约简的问题，一些增量属性约简的算法被提出.然而，对于分布决策信息系统增量属性约简的算法研究却甚少.为了有效解决动态分布决策信息系统约简更新的问题，提出了分布决策信息系统增量属性约简的方法.首先，本文介绍了分布决策系统的相关概念，然后，给出了分布决策信息系统基于矩阵方法的知识粒度增量更新机制，并设计了分布决策信息系统基于矩阵方法的增量属性约简算法.最后，从UCI机器学习库下载一些数据集，分别用增量和非增量属性约简算法进行对比实验，实验结果说明了增量属性约简算法比非增量约简算法在计算时间上有较强的优势.

Abstract

How to effectively carry out attribute reduction is an important task when some data vary dynamically in the decision system

many incremental attribute reduction methods have been proposed. Whereas there is less work on incremental attribute reduction method for distributed decision information system. In order to address this problem

a matrix-based incremental attribute reduction algorithm is proposed for distributed data. In this paper

we first introduce some definitions and conceptions of distributed decision information system. Then

the matrix-based incremental mechanisms for knowledge granularity are discussed and the corresponding matrix-based incremental attribute reduction algorithm for updating reduct is developed. Finally

some data sets is downloaded from the UCI machine learning libraries and some numerical experiments demonstrate that the incremental attribute reduction algorithm is effective and efficient compared to non-incremental attribute reduction algorithm.

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references

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