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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-09-18
摘要
Motivation The gut microbiome plays an important role in human health and 疾病, prompting large-scale studies that generate extensive datasets. A critical preprocessing step in analysing such datasets is anomaly detection, which aims to identify erroneous samples and prevent misleading 統計 outcomes. Microbiome data, however, pose unique challenges such as compositionality, sparsity, interdependencies, and high dimensionality, limiting the effectiveness of conventional methods and highlighting the need for specifically-tailored approaches for anomaly detection in microbiome data.Implementation To address this challenge, we introduce KADAIF, a microbiome-specific anomaly detection method that generalizes the common Isolation 森林 (IF) approach. As in IF, KADAIF builds an ensemble of trees, each r
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