...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Soft Computing >A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy
【24h】

A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy

机译：融合新型局部搜索策略的混合粒子群算法用于特征子集选择

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Feature selection has been widely used in data mining and machine learning tasks to make a model with a small number of features which improves the classifier's accuracy. In this paper, a novel hybrid feature selection algorithm based on particle swarm optimization is proposed. The proposed method called HPSO-LS uses a local search strategy which is embedded in the particle swarm optimization to select the less correlated and salient feature subset. The goal of the local search technique is to guide the search process of the particle swarm optimization to select distinct features by considering their correlation information. Moreover, the proposed method utilizes a subset size determination scheme to select a subset of features with reduced size. The performance of the proposed method has been evaluated on 13 benchmark classification problems and compared with five state-of-the-art feature selection methods. Moreover, HPSO-LS has been compared with four well-known filter-based methods including information gain, term variance, fisher score and mRMR and five well-known wrapper-based methods including genetic algorithm, particle swarm optimization, simulated annealing and ant colony optimization. The results demonstrated that the proposed method improves the classification accuracy compared with those of the filter based and wrapper-based feature selection methods. Furthermore, several performed statistical tests show that the proposed method's superiority over the other methods is statistically significant. (C) 2016 Elsevier B.V. All rights reserved.
机译：特征选择已广泛用于数据挖掘和机器学习任务中，以创建具有少量特征的模型，从而提高了分类器的准确性。提出了一种基于粒子群算法的混合特征选择算法。所提出的称为HPSO-LS的方法使用嵌入在粒子群优化中的局部搜索策略来选择相关性和显着性较低的特征子集。局部搜索技术的目标是指导粒子群优化的搜索过程，通过考虑相关特征来选择不同的特征。此外，所提出的方法利用子集大小确定方案来选择具有减小的大小的特征的子集。该方法的性能已针对13个基准分类问题进行了评估，并与五种最新的特征选择方法进行了比较。此外，HPSO-LS已与四种基于过滤器的著名方法（包括信息增益，项方差，fisher得分和mRMR）进行了比较，并与五种基于包装器的著名方法（包括遗传算法，粒子群优化，模拟退火和蚁群）进行了比较。优化。结果表明，与基于过滤器和基于包装的特征选择方法相比，该方法提高了分类精度。此外，一些进行的统计测试表明，所提出的方法相对于其他方法的优越性在统计学上是有意义的。 （C）2016 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号