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首页> 外文会议>2019 International Conference on High Performance Big Data and Intelligent Systems >Improved Competitive Swarm Optimization Algorithms for Feature Selection
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Improved Competitive Swarm Optimization Algorithms for Feature Selection

机译：特征选择的改进竞争群优化算法

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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，已应用于特征选择，并且在具有高特征维的数据集上表现良好。作为一种包装方法，CSO面临着计算昂贵且耗时的问题。为了解决这些问题，从减少粒子适应度计算时间的角度出发，我们提出了两种改进的算法：FCSO（快速竞争群优化）和SFCSO（选定的快速竞争群优化）。 FCSO通过减少每次迭代中涉及的粒子数量来缩短运行所需的时间，而SFCSO通过筛选机制可以达到相同的目标。 SFCSO提高了FCSO的稳定性。我们使用KNN分类器对四个具有不同大小和维度的数据集进行实验。实验结果表明，FCSO将时间减少到原始时间的十分之一，而SFCSO减少了一半。 FCSO在二元分类问题上具有更好的性能，而SFCSO在多元分类问题上具有更好的性能。两种算法都可以显着降低时间复杂度，但精度会略有下降，甚至更高，这是可以接受的。

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