• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on High Performance Big Data and Intelligent Systems >Improved Competitive Swarm Optimization Algorithms for Feature Selection
【24h】

Improved Competitive Swarm Optimization Algorithms for Feature Selection

机译：改进了特征选择的竞争性群优化算法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

CSO is an optimization algorithm based on the competition concept, which has been applied to feature selection and had a good performance on datasets with high feature dimensions. As a wrapper method, CSO is confronted with the problem of computation expensive and time consuming. To solve the problems, we proposed two improved algorithms from the perspective of reducing particle fitness calculation times: FCSO (Faster Competitive Swarm Optimization) and SFCSO (Selected Faster Competitive Swarm Optimization). FCSO shortens the time required for running by reducing the number of particles involved in each iteration, whereas SFCSO achieves the same goal by screening mechanism. SFCSO improves the stability of FCSO. We use KNN classifier to carry out experiments on four datasets with different size and dimension. The experimental results show that FCSO reduced time to one-tenth of the original, while SFCSO was half of it. FCSO has a better performance on binary classification problems so as SFCSO in multi-classification problems. Both algorithms can significantly reduce time complexity with a little decline of accuracy or even a higher accuracy, which is acceptable.
机译：CSO是一种基于竞争概念的优化算法，它已应用于特征选择，并且在具有高特征尺寸的数据集中具有良好的性能。作为包装方法，CSO面对计算昂贵且耗时的计算问题。为了解决上述问题，我们提出了两种改进算法从减少颗粒适应度计算倍视角：FCSO（更快的竞争群优化）和SFCSO（选择的更快竞争群优化）。 FCSO缩短了通过减少每次迭代中涉及的粒子的数量来缩短运行所需的时间，而SFCSO通过筛选机制实现了相同的目标。 SFCSO提高了FCSO的稳定性。我们使用KNN分类器在具有不同尺寸和尺寸的四个数据集上进行实验。实验结果表明，FCSO还将时间减少到原件的十分之一，而SFCSO是其中的一半。 FCSO在二进制分类问题上具有更好的性能，因此在多分类问题中的SFCSO。这两种算法都可以显着降低时间复杂度，精度略有下降，甚至是更高的准确性，这是可接受的。

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