Outlier detection in tabular data is crucial for safeguarding data integrity in high-stakes domains such as cybersecurity, financial fraud detection, and healthcare, where anomalies can cause serious operational and economic impacts. Despite advances in both data mining and deep learning, many existing methods struggle with mixed-type tabular data, often relying on encoding schemes that lose important semantic information. Moreover, they frequently lack interpretability, offering little insight into which specific values cause anomalies. To overcome these challenges, we introduce \textsf{\textbf{RFOD}}, a novel \textsf{\textbf{R}}andom \textsf{\textbf{F}}orest-based \textsf{\textbf{O}}utlier \textsf{\textbf{D}}etection framework tailored for tabular data. Rather than modeling a global joint distribution, \textsf{RFOD} reframes anomaly detection as a feature-wise conditional reconstruction problem, training dedicated random forests for each feature conditioned on the others. This design robustly handles heterogeneous data types while preserving the semantic integrity of categorical features. To further enable precise and interpretable detection, \textsf{RFOD} combines Adjusted Gower's Distance (AGD) for cell-level scoring, which adapts to skewed numerical data and accounts for categorical confidence, with Uncertainty-Weighted Averaging (UWA) to aggregate cell-level scores into robust row-level anomaly scores. Extensive experiments on 15 real-world datasets demonstrate that \textsf{RFOD} consistently outperforms state-of-the-art baselines in detection accuracy while offering superior robustness, scalability, and interpretability for mixed-type tabular data.

翻译：表格数据中的异常检测对于维护网络安全、金融欺诈检测和医疗健康等高风险领域的数据完整性至关重要，这些领域的异常可能导致严重的运营和经济影响。尽管数据挖掘和深度学习领域已取得进展，但现有方法在处理混合类型表格数据时仍面临挑战，通常依赖于会丢失重要语义信息的编码方案。此外，这些方法往往缺乏可解释性，难以揭示导致异常的具体数值特征。为应对这些挑战，本文提出\textsf{\textbf{RFOD}}——一种专为表格数据设计的、基于\textsf{\textbf{随机森林}}的\textsf{\textbf{异常检测}}新框架。与建模全局联合分布不同，\textsf{RFOD}将异常检测重构为特征维度的条件重建问题，通过训练基于其他特征条件的独立随机森林来处理每个特征。该设计在保持分类特征语义完整性的同时，能稳健处理异构数据类型。为实现精确且可解释的检测，\textsf{RFOD}结合了适用于偏态数值数据并考虑分类置信度的单元格级评分方法——调整高氏距离（AGD），以及通过不确定性加权平均（UWA）将单元格级评分聚合为稳健的行级异常分数。在15个真实数据集上的大量实验表明，\textsf{RFOD}在检测精度上持续优于当前最先进的基线方法，同时对混合类型表格数据展现出更优的鲁棒性、可扩展性和可解释性。