Feature selection is an important task in machine learning that has two main objectives: 1) reducing dimensionality and 2) improving learning performance. Feature selection can be considered a multiobjective problem. However, it has its problematic characteristics, such as a highly discontinuous Pareto front, imbalance preferences, and partially conflicting objectives. These characteristics are not easy for existing evolutionary multiobjective optimization (EMO) algorithms. We propose a new decomposition approach with two mechanisms (static and dynamic) based on multiple reference points under the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to address the above-mentioned difficulties of feature selection. The static mechanism alleviates the dependence of the decomposition on the Pareto front shape and the effect of the discontinuity. The dynamic one is able to detect regions in which the objectives are mostly conflicting, and allocates more computational resources to the detected regions. In comparison with other EMO algorithms on 12 different classification datasets, the proposed decomposition approach finds more diverse feature subsets with better performance in terms of hypervolume and inverted generational distance. The dynamic mechanism successfully identifies conflicting regions and further improves the approximation quality for the Pareto fronts.

中文翻译：

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基于多参考点的分类多目标特征选择分解：静态和动态机制

特征选择是机器学习中的一项重要任务，它有两个主要目标：1）降低维度和 2）提高学习性能。特征选择可以被认为是一个多目标问题。然而，它有其问题特征，例如高度不连续的帕累托前沿、不平衡的偏好和部分冲突的目标。这些特征对于现有的进化多目标优化 (EMO) 算法来说并不容易。在基于分解的多目标进化算法（MOEA/D）框架下，我们提出了一种基于多个参考点的静态和动态两种机制的新分解方法，以解决上述特征选择的困难。静态机制减轻了分解对帕累托前沿形状的依赖性和不连续性的影响。动态的能够检测目标最冲突的区域，并为检测到的区域分配更多的计算资源。与在 12 个不同分类数据集上的其他 EMO 算法相比，所提出的分解方法找到了更多样的特征子集，在超体积和反向代距方面具有更好的性能。动态机制成功地识别了冲突区域，并进一步提高了帕累托前沿的近似质量。与在 12 个不同分类数据集上的其他 EMO 算法相比，所提出的分解方法找到了更多样的特征子集，在超体积和反向代距方面具有更好的性能。动态机制成功地识别了冲突区域，并进一步提高了帕累托前沿的近似质量。与 12 个不同分类数据集上的其他 EMO 算法相比，所提出的分解方法发现了更多样的特征子集，在超体积和反向代距方面具有更好的性能。动态机制成功地识别了冲突区域，并进一步提高了帕累托前沿的近似质量。