Feature selection is a complex optimization problem with important real-world applications. Normally, its main target is to reduce the dimensionality of the dataset and increase the effectiveness of the classification. Owing to the population-inspired characteristics, different evolutionary algorithms (EAs) have been proposed to solve feature selection problems over the past decades. However, the majority of them only consider single objective optimization while many real-world problems have multiple objectives, which creates a genuine demand for designing more suitable and effective EAs to handle multi-objective feature selection. A multi-objective feature selection problem usually consists of two objectives: one is to minimize the number of selected features and the other is to minimize the error of classification. In this paper, we propose a duplication analysis based evolutionary algorithm (termed DAEA) for bi-objective feature selection in classification. In the proposed algorithm, we make improvements on the basic dominance-based EA framework in three aspects: first, the reproduction process is modified to improve the quality of offspring; second, a duplication analysis method is proposed to filter out the redundant solutions; third, a diversity based selection method is adopted to further select the reserved solutions. In the experiments, we have compared the proposed algorithm with five state-of-the-art multi-objective evolutionary algorithms (MOEAs) and tested them on 20 classification datasets, using two widely-used performance metrics. According to the empirical results, DAEA performs the best on most datasets, indicating that DAEA not only gains outstanding optimization performance but also obtains good classification and generalisation results.

中文翻译：

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一种基于重复分析的双目标特征选择进化算法

特征选择是一个复杂的优化问题，具有重要的实际应用。通常，其主要目标是降低数据集的维数并提高分类的有效性。由于人口启发的特征，在过去的几十年里，已经提出了不同的进化算法（EA）来解决特征选择问题。然而，他们中的大多数只考虑单目标优化，而许多现实世界的问题有多个目标，这就产生了设计更合适、更有效的 EA 来处理多目标特征选择的真正需求。一个多目标特征选择问题通常由两个目标组成：一个是最小化选择特征的数量，另一个是最小化分类误差。在本文中，我们提出了一种基于重复分析的进化算法（称为 DAEA），用于分类中的双目标特征选择。在所提出的算法中，我们在三个方面对基本的基于优势的 EA 框架进行了改进：第一，修改繁殖过程以提高后代质量；其次，提出了一种重复分析方法来滤除冗余解；第三，采用基于多样性的选择方法来进一步选择保留的解决方案。在实验中，我们将所提出的算法与五种最先进的多目标进化算法 (MOEA) 进行了比较，并使用两种广泛使用的性能指标在 20 个分类数据集上对其进行了测试。根据实证结果，DAEA 在大多数数据集上表现最好，