Value Feature selection is an effective method to solve the curse of dimensionality, which widely employs Evolutionary Computation (EC), such as Genetic Algorithms (GA), by regarding feature subsets as individuals. However, it is impossible for EC based feature selection approaches to possess big population sizes because of very long and infeasible computational time. We have proposed a method screening individuals by estimating their classification performances rapidly instead of deriving theirs with a certain classifier dilatorily. Consequently, aiming at improving classification accuracies, we propose an approach named as FS-NN-GA (Feature Selection approach based on Neural Networks and Genetic Algorithms) in this work. The proposed approach employs the neural networks trained with some randomly generated individuals, and their actual classification accuracies to estimate individuals’ classification accuracies and screens them in each round of GA. The individuals with low estimated accuracies are directly eliminated. Only a small number of individuals with high estimated accuracies are reserved, evaluated by deriving their accuracies with a certain classifier, and participate GA operations to be explored emphatically. As a result, big population sizes become feasible, and a huge number of individuals can be considered by GA in acceptable and feasible time, which improves performances of GA and derives high accuracies. We perform the experiments with 10 data sets in comparison with 11 available approaches. The experimental results show that FS-NN-GA outperforms other approaches on most data sets.

中文翻译：

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一种将神经网络与遗传算法相结合的特征选择方法

值特征选择是解决维数诅咒的有效方法，通过将特征子集视为个体，它广泛采用了进化计算（EC），例如遗传算法（GA）。但是，基于EC的特征选择方法不可能拥有很大的人口规模，因为计算时间非常长且不可行。我们提出了一种通过快速估计他们的分类表现来筛选个人的方法，而不是用某个分类器来不断地推导他们的方法。因此，为了提高分类的准确性，我们在这项工作中提出了一种名为FS-NN-GA（基于神经网络和遗传算法的特征选择方法）的方法。拟议的方法采用了由一些随机生成的个体训练的神经网络，以及他们的实际分类准确性，以估算个人的分类准确性，并在每轮GA中对其进行筛选。估计准确性较低的个人将被直接淘汰。仅保留少数具有较高估计准确度的个人，通过使用特定分类器推导其准确度来进行评估，并参与要重点研究的GA操作。结果，大人口规模变得可行，遗传算法可以在可接受且可行的时间内考虑到大量的个体，从而提高了遗传算法的性能并获得了较高的准确性。与11种可用方法相比，我们用10个数据集进行了实验。实验结果表明，FS-NN-GA在大多数数据集上均优于其他方法。