Feature Selection of Biomedical Literature by Membrane Particle Swarm Optimizer and Information Entropy

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Feature Selection of Biomedical Literature by Membrane Particle Swarm Optimizer and Information Entropy

更新时间：2025-07-09

- Feature Selection of Biomedical Literature by Membrane Particle Swarm Optimizer and Information Entropy
- Vol. 46, Issue 4, Pages: 45-51(2012)
- 作者机构：
  
  西安电子科技大学计算机学院,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP301
- Online First：10 April 2012，
  
  Published：2012
- 稿件说明：
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Feature Selection of Biomedical Literature by Membrane Particle Swarm Optimizer and Information Entropy[J]. 2012, 46(4): 45-51.
DOI：

Feature Selection of Biomedical Literature by Membrane Particle Swarm Optimizer and Information Entropy[J]. 2012, 46(4): 45-51. DOI：

摘要

针对生物医学文本挖掘中有效特征选择困难的问题

提出了一种新的基于膜粒子群优化和信息熵的文本特征选择方法.该方法以文本信息熵总和为目标函数

以膜系统的层次结构作为框架

以膜系统的消息传递机制作为进化方向

以粒子群优化进化作为进化规则.作为进化规则的粒子群优化算法

分别以局部搜索速率和全局搜索速率搜索得到文本信息熵最大的解

并在不同膜区域之间传递搜索结果

直到膜区域之间的消息传递结束或者达到限定的迭代次数.实验结果表明

利用提出的方法对医学文本特征进行选择后

对其进行分类

能使分类精确度和召回率分别提高2%和3%左右.

Abstract

To select the effective features of biomedical literature

a novel particle swarm optimizer based on membrane system is proposed. This scheme utilizes total text information entropy as the fitness function

employs the structure of membrane system as the frame

sets mechanism of message transferring as the propagation direction

and considers particle swarm optimizer as the iteration rule. The particle swarm optimizer searches the best solution maximizing the total text information entropy at local and global searching rates simultaneously

and exchanges solutions between membrane regions till the communication between regions stops or iteration reaches the limit times. The experiment shows that feature selection by the scheme improves the precession and heightens the classification recall of biomedical literature by 2% and 3%.

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references

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