Lee Jaesung, Park Jaegyun, Kim Hae-Cheon, Kim Dae-Won
School of Computer Science and Engineering, Chung-Ang University, 221, Heukseok-Dong, Dongjak-Gu, Seoul 06974, Korea.
Entropy (Basel). 2019 Jun 18;21(6):602. doi: 10.3390/e21060602.
Multi-label feature selection is an important task for text categorization. This is because it enables learning algorithms to focus on essential features that foreshadow relevant categories, thereby improving the accuracy of text categorization. Recent studies have considered the hybridization of evolutionary feature wrappers and filters to enhance the evolutionary search process. However, the relative effectiveness of feature subset searches of evolutionary and feature filter operators has not been considered. This results in degenerated final feature subsets. In this paper, we propose a novel hybridization approach based on competition between the operators. This enables the proposed algorithm to apply each operator selectively and modify the feature subset according to its relative effectiveness, unlike conventional methods. The experimental results on 16 text datasets verify that the proposed method is superior to conventional methods.
多标签特征选择是文本分类中的一项重要任务。这是因为它能使学习算法专注于预示相关类别的关键特征，从而提高文本分类的准确性。最近的研究考虑了进化特征包装器和过滤器的混合，以增强进化搜索过程。然而，尚未考虑进化算子和特征过滤算子的特征子集搜索的相对有效性。这导致最终特征子集退化。在本文中，我们提出了一种基于算子间竞争的新型混合方法。与传统方法不同，这使得所提出的算法能够有选择地应用每个算子，并根据其相对有效性修改特征子集。在16个文本数据集上的实验结果验证了所提出的方法优于传统方法。
