A fuzzy similarity information entropy attribute reduction algorithm 
for medical sensing diagnosis information systems

AO Qi; ZHANG Chenggang; HANG Gang; QU Bingjie

Chinese

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A fuzzy similarity information entropy attribute reduction algorithm for medical sensing diagnosis information systems

Calculation & Test | 更新时间：2026-05-18

- A fuzzy similarity information entropy attribute reduction algorithm for medical sensing diagnosis information systems
- Vol. 45, Issue 5, Pages: 160-166(2026)
- 作者机构：
  
  1. 鄂尔多斯职业学院信息工程系,内蒙古,鄂尔多斯,017010
  2. 内蒙古民族大学计算机科学与技术学院,内蒙古,通辽,028000
  3. 辽宁工程技术大学电气与控制工程学院,辽宁,阜新,123000
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP212;TP181
- Published：2026
- 稿件说明：
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AO Qi, ZHANG Chenggang, HANG Gang, et al. A fuzzy similarity information entropy attribute reduction algorithm for medical sensing diagnosis information systems[J]. 2026, 45(5): 160-166.
DOI：

AO Qi, ZHANG Chenggang, HANG Gang, et al. A fuzzy similarity information entropy attribute reduction algorithm for medical sensing diagnosis information systems[J]. 2026, 45(5): 160-166. DOI：

摘要

属性约简对于资源受限的微系统实现高效精准的决策至关重要，然而传统的属性约简方法难以处理连续与离散数据并存的混合型信息系统，为此，提出一种基于模糊相似关系条件熵的混合数据属性约简方法。针对混合型信息系统的特点，首先将等价关系和邻域关系进行结合和改进，提出了一种新的模糊相似关系。然后基于此关系，建立了混合型信息系统的模糊相似关系粒结构模型，并定义了模糊相似关系的信息量与信息熵度量。最后利用模糊相似关系条件熵度量模型，设计了一种新的属性约简算法。在典型医疗传感诊断数据集上的实验结果表明，所提算法的有效性得到了验证，为微系统感知数据的轻量化处理与智能分析提供了一种实用途径。

Abstract

Attribute reduction is crucial for resource-constrained microsystems to achieve efficient and accurate decision-making.However

traditional attribute reduction methods struggle to handle hybrid information systems with coexisting continuous and discrete data.To address this issue

a hybrid data attribute reduction method based on fuzzy similarity relation conditional entropy is proposed.Given the characteristics of hybrid information systems

equivalence relations and neighborhood relations are integrated and improved first

and a novel fuzzy similarity relation is proposed.Building upon this relation

a fuzzy similarity relation granular structure model is established for hybrid information systems

and the metrics for fuzzy similarity relation information quantity and information entropy are defined.Finally

by utilizing the fuzzy similarity relation conditional entropy metric model

a new attribute reduction algorithm is designed.Experimental results on typical medical sensing diagnosis datasets demonstrate the effectiveness of the proposed algorithm

which provides a practical approach for lightweight processing and intelligent analysis of sensing data in microsystems.

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