FSEVAL工具箱让特征选择算法评估更高效直观。
FSEVAL: Feature Selection Evaluation Toolbox and Dashboard
- 提供统一评估框架，支持监督与无监督场景
- 集成可视化仪表盘，一键生成评估报告
- 适合研究特征选择的学者快速对比算法
特征选择是机器学习与数据挖掘中的基础任务，旨在从冗余特征中识别出有用信息。它通过移除冗余特征缓解维度灾难问题，同时保留可解释性，不同于降维方法。特征选择在有监督和无监督设置下均有应用，需依赖不同评估指标判断算法优劣。本文提出FSEVAL——一个配套可视化仪表盘的特征选择评估工具箱，旨在实现特征选择算法的全面、标准化评估。FSEVAL提供统一、易用的评估与可视化平台，帮助研究人员便捷开展大规模、系统性的算法比较。
原文摘要 · Abstract (English)
Feature selection is a fundamental machine learning and data mining task, involved with discriminating redundant features from informative ones. It is an attempt to address the curse of dimensionality by removing the redundant features, while unlike dimensionality reduction methods, preserving explainability. Feature selection is conducted in both supervised and unsupervised settings, with different evaluation metrics employed to determine which feature selection algorithm is the best. In this paper, we propose FSEVAL, a feature selection evaluation toolbox accompanied with a visualization dashboard, with the goal to make it easy to comprehensively evaluate feature selection algorithms. FSEVAL aims to provide a standardized, unified, evaluation and visualization toolbox to help the researchers working in the field, conduct extensive and comprehensive evaluation of feature selection algorithms with ease.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。