...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Chemometrics and Intelligent Laboratory Systems >Feature selection using particle swarm optimization-based logistic regression model
【24h】

Feature selection using particle swarm optimization-based logistic regression model

机译：使用基于粒子群优化的Logistic回归模型的功能选择

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

In any classification problem, the dataset typically has a large number of features. However, not all features are necessary to obtain a good classification performance because some of them are irrelevant and redundant. Therefore, classifiers with less number of features but with better classification accuracy are favored for ease of interpretation. In this work, particle swarm optimization algorithm along with logistic regression model is proposed. Additionally, the Bayesian information criterion (BIC) as a fitness function is proposed. The performance of different fitness functions is investigated and compared with BIC. The performance of the proposed method is evaluated based on a large number of different types of datasets. Experimental results using different types of datasets demonstrate the usefulness of our proposed method in significantly obtaining an improved classification performance with few features. Further, the results show that the proposed methods have a competitive performance comparing with other existing fitness functions.
机译：在任何分类问题中，数据集通常具有大量特征。但是，并非所有特征都必须获得良好的分类性能，因为其中一些是无关紧要的和多余的。因此，为了易于解释，有利于更少的特征但具有更好分类准确性的分类器。在这项工作中，提出了粒子群优化算法以及Logistic回归模型。另外，提出了贝叶斯信息标准（BIC）作为健身功能。研究了不同健身功能的性能并与BIC进行比较。基于大量不同类型的数据集来评估所提出的方法的性能。使用不同类型数据集的实验结果证明了我们提出的方法的有用性，在很大程度上在很大程度上获得了改进的分类性能。此外，结果表明，该方法具有与其他现有健身功能相比的竞争性能。

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