ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

By Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, George H. Chen. IEEE Transactions on Knowledge and Data Engineering, 2022

500 citations recorded by the source corpus.

Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional datasets. To address these issues, we present a simple yet effective algorithm called ECOD (Empirical-Cumulative-distribution-based Outlier Detection), which is inspired by the fact that outliers are often the “rare events” that appear in the tails of a distribution. In a nutshell, ECOD first estimates the underlying distribution of the input data in a nonparametric fashion by computing the empirical cumulative distribution per dimension of the data. ECOD then uses these empirical distributions to estimate tail probabilities per dimension for each data point. Finally, ECOD computes an outlier score of each data point by aggregating estimated tail probabilities across dimensions. Our contributions are as follows: (1) we propose a novel outlier detection method called ECOD, which is both parameter-free and easy to interpret; (2) we perform extensive experiments on 30 benchmark datasets, where we find that ECOD outperforms 11 state-of-the-art baselines in terms of accuracy, efficiency, and scalability; and (3) we release an easy-to-use and scalable (with distributed support) Python implementation for accessibility and reproducibility.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/5a212bb4307008f27018edc0b8d873f4e9657d17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiyang Hu
2. Songqiao Han
3. Hailiang Huang
4. Mingqi Jiang
5. Yue Zhao
6. Yongjun Wang
7. Yijie Wang
8. Hongzuo Xu

In Civil and Structural Engineering

1. Guansong Pang

In Control and Systems Engineering

1. Yue Zhao

Citations in the corpus

• ADBench: Anomaly Detection Benchmark (2022, methodology): “…to the previous benchmarks, we have a larger algorithm collection with ( i ) the latest unsupervised AD algorithms like DeepSVDD [151] and ECOD [97]; ( ii ) SOTA semi-supervised algorithms, including DeepSAD [152] and DevNet [131]; ( iii ) latest network architectures like ResNet [62] in…”
• Deep Isolation Forest for Anomaly Detection (2022, background): “This section further justifies this intuition by investigating the effectiveness of combining random representations with other anomaly scoring methods, including probability-based anomaly scoring method ECOD [46], distance-based method KNN [47], and density-based method LOF [48].”