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A feature selection approach combining neural networks with genetic algorithms

机译：一种特征选择方法与遗传算法结合神经网络

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摘要

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.
机译：值特征选择是解决维度诅咒的有效方法，其广泛采用遗传算法（eC），例如遗传算法（GA），关于特征子集作为个体。然而，由于具有很长的计算时间，因此基于EC的特征选择方法具有大群尺寸。我们提出了一种通过迅速估计其分类性能而不是将它们的分类，而不是解除某个分类器来筛选个人的方法。因此，旨在提高分类准确性，我们提出了一种作为FS-NN-GA（基于神经网络和遗传算法的特征选择方法）的方法。拟议的方法采用与某些随机产生的个人培训的神经网络，以及它们的实际分类精度来估计个人的分类准确性，并在每轮GA中筛选它们。估计精度低的个体直接被淘汰。只保留少数具有高估计精度的个体，通过使用某个分类器的准确性评估，并重点探讨它们的准确性。结果，大群尺寸变得可行，并且可以在可接受和可行的时间内考虑大量的个体，这改善了GA的性能并导出了高精度。与11种可用方法相比，我们使用10个数据集进行实验。实验结果表明，FS-NN-GA优于大多数数据集上的其他方法。

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