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<p>Until now, a number of one-class classification methods have been used for anomaly detection problems. However, only single one-class classifiers often fail to accurately detect anomalous patterns because they might not be optimized to describe inherent target class structures. To achieve more superior anomaly detection performance, we propose random neighborhood ensemble-based one-class classification algorithm. In the proposed algorithm, multiple one-class classifiers are obtained from various random <i>k</i>-NN sets identified by ensemble of random subspace and random neighborhood size approaches, and they are finally aggregated as novelty score. The random subspace and random neighborhood size approaches helps to diversify <i>k</i>-NN sets, and these diversified <i>k</i>-NN sets can effectively accommodate the inherent target class structures having complex patterns. In this study, we conducted experimental studies with various benchmark datasets to investigate the characteristics of the proposed algorithm and compare it with existing methods. The experimental results demonstrate that the proposed algorithm performs better or comparable to existing one-class classification methods in most of cases.</p>

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Random neighborhood ensemble-based one-class classification algorithm for anomaly detection

  • 期刊论文

  • 发表日期：

    2026年1月3日

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  • Sungho Park,
  • Jaehong Yu

摘要

Until now, a number of one-class classification methods have been used for anomaly detection problems. However, only single one-class classifiers often fail to accurately detect anomalous patterns because they might not be optimized to describe inherent target class structures. To achieve more superior anomaly detection performance, we propose random neighborhood ensemble-based one-class classification algorithm. In the proposed algorithm, multiple one-class classifiers are obtained from various random k-NN sets identified by ensemble of random subspace and random neighborhood size approaches, and they are finally aggregated as novelty score. The random subspace and random neighborhood size approaches helps to diversify k-NN sets, and these diversified k-NN sets can effectively accommodate the inherent target class structures having complex patterns. In this study, we conducted experimental studies with various benchmark datasets to investigate the characteristics of the proposed algorithm and compare it with existing methods. The experimental results demonstrate that the proposed algorithm performs better or comparable to existing one-class classification methods in most of cases.