Isolation Forest in Novelty Detection Scenario

• URL: http://arxiv.org/abs/2505.08489v1
• Date: Tue, 13 May 2025 12:21:53 GMT
• Title: Isolation Forest in Novelty Detection Scenario
• Authors: Adam Ulrich, Jan Krňávek, Roman Šenkeřík, Zuzana Komínková Oplatková, Radek Vala,
• Abstract summary: novelty detection focuses on identifying previously unseen patterns after training solely on regular data.<n>In this work, we explore the Half-Space Tree (HST) algorithm, originally proposed for streaming anomaly detection.<n>We propose a novel theoretical modification to adapt it specifically for novelty detection tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Data mining offers a diverse toolbox for extracting meaningful structures from complex datasets, with anomaly detection emerging as a critical subfield particularly in the context of streaming or real-time data. Within anomaly detection, novelty detection focuses on identifying previously unseen patterns after training solely on regular data. While classic algorithms such as One-Class SVM or Local Outlier Factor (LOF) have been widely applied, they often lack interpretability and scalability. In this work, we explore the Half-Space Tree (HST) algorithm, originally proposed for streaming anomaly detection, and propose a novel theoretical modification to adapt it specifically for novelty detection tasks. Our approach is grounded in the idea that anomalies i.e., novelties tend to appear in the higher leaves of the tree, which are less frequently visited by regular instances. We analytically demonstrate the effectiveness of this approach using probabilistic analysis, expected depth (EXD) calculations, and combinatorial reasoning. A comparative analysis of expected depths between our modified HST and the original Isolation Forest highlights that novelty points are significantly more isolated in our approach. This supports the hypothesis that HSTs, with appropriate structural adaptation, can serve as interpretable and efficient novelty detectors. The paper contributes a theoretical foundation and supporting analysis for this adaptation, setting the stage for further application and experimentation.

Related papers

• Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better [63.567886330598945]
  Infrared small target (IRST) detection is challenging in simultaneously achieving precise, universal, robust and efficient performance.<n>Current learning-based methods attempt to leverage more" information from both the spatial and the short-term temporal domains.<n>We propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection.
  arXiv  Detail & Related papers  (2025-06-15T08:19:32Z)
• Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon [1.8816077341295625]
  This paper introduces a novel approach for time series anomaly prediction, incorporating temporal information directly into the prediction results. Our results demonstrate the efficacy of our approach in providing timely and accurate anomaly predictions, setting a new benchmark for future research in this field.
  arXiv  Detail & Related papers  (2024-08-08T11:22:52Z)
• Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning [50.84938730450622]
  We propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios. Our method can be extended to more applications with high-density features in output spaces, such as multiple-choice questions.
  arXiv  Detail & Related papers  (2024-05-22T22:22:25Z)
• Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection [3.7384109981836158]
  We study the problem of out-of-distribution (OOD) detection in reinforcement learning (RL) We propose a clarification of terminology for OOD detection in RL, which aligns it with the literature from other machine learning domains. We present new benchmark scenarios for OOD detection, which introduce anomalies with temporal autocorrelation into different components of the agent-environment loop. We find that DEXTER can reliably identify anomalies across benchmark scenarios, exhibiting superior performance compared to both state-of-the-art OOD detectors and high-dimensional changepoint detectors adopted from statistics.
  arXiv  Detail & Related papers  (2024-04-10T15:39:49Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection [64.21963650519312]
  Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space. Our approach establishes a new state-of-the-art across multiple benchmarks, handling diverse anomaly types.
  arXiv  Detail & Related papers  (2023-10-01T21:24:05Z)
• Sequential Attention Source Identification Based on Feature Representation [88.05527934953311]
  This paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge.
  arXiv  Detail & Related papers  (2023-06-28T03:00:28Z)
• Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection [0.0]
  Time series anomaly detection (TSAD) plays a vital role in many industrial applications.<n>Contrastive learning has gained momentum in the time series domain for its prowess in extracting meaningful representations from unlabeled data.<n>In this study, we propose a novel approach, CNT, that incorporates a window-based contrastive learning strategy fortified with learnable transformations.
  arXiv  Detail & Related papers  (2023-04-16T21:36:19Z)
• Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
  We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
  arXiv  Detail & Related papers  (2022-10-27T19:45:12Z)
• Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces [0.9543943371833464]
  We present EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces) It adds explainability to the predictions obtained with the original model. We report experimental results on extensive real-world data, particularly in the domain of network intrusion detection.
  arXiv  Detail & Related papers  (2022-09-09T08:20:13Z)
• Stochastic Functional Analysis and Multilevel Vector Field Anomaly Detection [0.0]
  We develop a novel analysis approach for detecting anomalies in massive vector field datasets. An optimal vector field Karhunen-Loeve (KL) expansion is applied to such random field data. The method is applied to the problem of deforestation and degradation in the Amazon forest.
  arXiv  Detail & Related papers  (2022-07-11T13:11:16Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Anomaly Detection with Test Time Augmentation and Consistency Evaluation [13.709281244889691]
  We propose a simple, yet effective anomaly detection algorithm named Test Time Augmentation Anomaly Detection (TTA-AD) We observe that in-distribution data enjoy more consistent predictions for its original and augmented versions on a trained network than out-distribution data. Experiments on various high-resolution image benchmark datasets demonstrate that TTA-AD achieves comparable or better detection performance.
  arXiv  Detail & Related papers  (2022-06-06T04:27:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.