Meta-Learning for Unsupervised Outlier Detection with Optimal Transport

Abstract

Automated machine learning has been widely researched and adopted in the field of supervised classification and regression, but progress in unsupervised settings has been limited. We propose a novel approach to automate outlier detection based on meta-learning from previous datasets with outliers. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport in particular, to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our approach and find that it outperforms the state of the art methods in unsupervised outlier detection. This approach can also be easily generalized to automate other unsupervised settings.

References in corpus (8)

• Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
• How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?
• PyOD: A Python Toolbox for Scalable Outlier Detection
• ADBench: Anomaly Detection Benchmark
• Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data
• Low-rank Optimal Transport: Approximation, Statistics and Debiasing
• A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
• Scrutinizing Shipment Records To Thwart Illegal Timber Trade