Immune Clone Feature Selection Method Combining Different Point Statistics of Overlap Region with Correlation Analysis

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Immune Clone Feature Selection Method Combining Different Point Statistics of Overlap Region with Correlation Analysis

更新时间：2025-05-08

- Immune Clone Feature Selection Method Combining Different Point Statistics of Overlap Region with Correlation Analysis
- Vol. 46, Issue 5, Pages: 50-56(2012)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH165.3
- Online First：10 May 2012，
  
  Published：2012
- 稿件说明：
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Immune Clone Feature Selection Method Combining Different Point Statistics of Overlap Region with Correlation Analysis[J]. 2012, 46(5): 50-56.
DOI：

Immune Clone Feature Selection Method Combining Different Point Statistics of Overlap Region with Correlation Analysis[J]. 2012, 46(5): 50-56. DOI：

摘要

为了解决机械故障诊断中的特征选择问题

利用免疫克隆选择算法

提出了一种结合交叠区异点统计和相关性分析的免疫克隆特征选择方法

可有效地去除不相关特征和冗余特征.基于空间分布的交叠区异点

设计了交叠区异点统计的优化指标; 基于J散度距离

设计了一种冗余特征的评估指标; 基于免疫克隆选择算法

结合交叠区异点统计优化指标和冗余特征评估指标

提出了一种免疫克隆特征选择新算法.仿真和实际工程应用的结果表明:文中提出的方法比常用的特征选择方法更加有效

选出的特征分类精度更高

特征子集更小

更能满足故障诊断的需求.

Abstract

To treat the feature selection for mechanical fault diagnosis

a novel immune clone feature selection method combining different point statistics of overlap region with correlation analysis is proposed with which the irrelevant features and redundant features can be removed effectively. According to the different point of the overlap region in space distribution

an optimization index is designed. An evaluation index of redundant features in correlation analysis following J-divergence is put forward. Then an immune clone feature selection strategy(ICFSS)is constructed with the two indexes. The simulation and industrial applications show its higher precision and smaller feature subset for more effective fault diagnosis.

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references

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