AFS-BM：通过带二进制掩码的自适应特征选择增强模型性能
AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking
AI总结：
针对特征选择在高维、可扩展性和特征重要性变化等方面的挑战，提出AFS-BM，通过联合优化与二进制掩码同步选择特征并训练模型，在真实竞赛数据集上提升准确率并降低计算复杂度。
摘要：
我们研究一般机器学习（ML）情境下的特征选择问题，这是该领域最关键的课题之一。尽管已经存在许多特征选择方法，但这些方法面临可扩展性、管理高维数据、处理相关特征、适应可变特征重要性以及整合领域知识等挑战。为此，我们提出了“带二进制掩码的自适应特征选择”（Adaptive Feature Selection with Binary Masking，AFS-BM），以解决这些问题。AFS-BM通过联合优化实现同时进行的特征选择和模型训练。具体而言，我们执行联合优化和二进制掩码，以便在训练过程中持续调整特征集合和模型参数。该方法显著提高了模型准确率，并降低了计算需求。我们提供了大量实验，使用来自真实竞赛的知名数据集，将AFS-BM与成熟的特征选择方法进行比较。结果表明，AFS-BM在准确率方面取得显著提升，并且所需计算复杂度明显更低。这归因于AFS-BM能够在训练过程中动态适应特征重要性的变化，这是对该领域的一项重要贡献。为保证结果的可复现性并促进进一步研究，我们公开发布代码。
英文摘要：
We study the problem of feature selection in general machine learning (ML) context, which is one of the most critical subjects in the field. Although, there exist many feature selection methods, however, these methods face challenges such as scalability, managing high-dimensional data, dealing with correlated features, adapting to variable feature importance, and integrating domain knowledge. To this end, we introduce the "Adaptive Feature Selection with Binary Masking" (AFS-BM) which remedies these problems. AFS-BM achieves this by joint optimization for simultaneous feature selection and model training. In particular, we do the joint optimization and binary masking to continuously adapt the set of features and model parameters during the training process. This approach leads to significant improvements in model accuracy and a reduction in computational requirements. We provide an extensive set of experiments where we compare AFS-BM with the established feature selection methods using well-known datasets from real-life competitions. Our results show that AFS-BM makes significant improvement in terms of accuracy and requires significantly less computational complexity. This is due to AFS-BM's ability to dynamically adjust to the changing importance of features during the training process, which an important contribution to the field. We openly share our code for the replicability of our results and to facilitate further research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
