With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved.

中文翻译：

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基于混沌聚类策略和小生境粒子群算法的特征选择优化

随着数据大小的迅速增加，对特征选择的需求不断增长，特征选择已成为处理高维数据的强大工具。在本文中，我们提出了一种基于混沌群的小生境粒子群优化的特征选择方法，用于评价特征选择算法的重要性。提出了一种迭代算法来优化新模型。已经证明，解决新模型等同于使用灵活且适应性强的范数正则化解决NP问题。首先，整个人口分为两组：NPSO组和混乱组。分别迭代这两个组，并更新全局优化。其次，NPSO基团与混沌基团的交叉重复避免了粒子陷入局部最优化。最后，在10个UCI数据集中选择了三种代表性算法进行比较。实验结果表明，该算法的特征选择性能优于比较算法，并且分类精度明显提高。