← Back to all news
arXiv Machine Learning By Lohith Srikanth Pentapalli, Josette Riep, Justin Ouwerkerk, Kelly Cohen

Bridging the Closed-World Gap: Interpretable Anomaly Detection via a Hybrid TSK-FCM Ensemble on the BETH Dataset

Read the original on arXiv Machine Learning →

The paper introduces a Hybrid Fuzzy Ensemble that combines a gradient‑optimized Takagi‑Sugeno‑Kang (TSK) fuzzy classifier with a parallel Fuzzy C‑Means (FCM) module to address the closed‑world gap in anomaly detection on the BETH dataset. It replaces traditional fuzzy membership scores with a robust Min Distance metric to mitigate the curse of dimensionality, achieving a state‑of‑the‑art AUROC of 0.9951 and outperforming Isolation Forest by 17.1%. The approach delivers near‑perfect out‑of‑distribution detection while maintaining interpretability through fuzzy logic.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 15

Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection

The paper introduces ISER, an isolation-based method for unsupervised tabular anomaly detection that uses hypersphere radii to encode local density and maintains linear time and constant space complexity. ISER builds ensemble representations where smaller radii indicate dense regions and larger radii indicate sparse regions, and it employs a similarity-based scoring method that compares these representations to a theoretical anomaly reference pattern. Experiments on 20 real-world datasets show that ISER outperforms 12 state‑of‑the‑art methods, including an enhanced Isolation Forest.

By Yang Cao, Sikun Yang, Hao Tian, Kai He, Lianyong Qi, Ming Liu, Yujiu Yang, Hong-Kun Zhang
efficiencybenchmarkssafety
More like this →
arXiv Machine Learning
Jul 13

XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles

arXiv:2605. 13922v2 Announce Type: replace-cross Abstract: During thDuring the last few years, the term Mechanistic Interpretability, a specific area, under the umbrella of explainable artificial intelligence (XAI), has been introduced, to explain the decisions made by complex machine learning (ML) models in critical systems like UAV intrusion detection systems (UAVIDS).

By Iakovos-Christos Zarkadis, Christos Douligeris
safety
More like this →