Feature selection is an important but often expensive process, especially with a large number of instances. This problem can be addressed by using a small training set, i.e. a surrogate set. In this work, we propose to use a hierarchical clustering method to build various surrogate sets, which allows to analyze the effect of surrogate sets with different qualities and quantities on the feature subsets. Further, a dynamic surrogate model is proposed to automatically adjust surrogate sets for different datasets. Based on this idea, a feature selection system is developed using particle swarm optimization as the search mechanism. The experiments show that the hierarchical clustering method can build better surrogate sets to reduce the computational time, improve the feature selection performance, and alleviate overfitting. The dynamic method can automatically choose suitable surrogate sets to further improve the classification accuracy.

中文翻译：

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具有用于特征选择的替代模型的PSO：基于静态和动态聚类的方法

特征选择是一个重要但通常很昂贵的过程，尤其是在大量实例中。可以通过使用小的训练集（即代理集）来解决此问题。在这项工作中，我们建议使用分层聚类方法来构建各种代理集，这可以分析具有不同质量和数量的代理集对特征子集的影响。此外，提出了一种动态代理模型来针对不同数据集自动调整代理集。基于这一思想，开发了一种以粒子群优化为搜索机制的特征选择系统。实验表明，层次聚类方法可以建立更好的代理集，减少计算时间，提高特征选择性能，减轻过度拟合。