Hyperparameter Optimization for Unsupervised Outlier Detection
This work addresses a key challenge in applying outlier detection to new datasets where labels are unavailable, offering a systematic solution that is incremental but practical for domain-specific applications.
The paper tackles the problem of optimizing hyperparameters for unsupervised outlier detection algorithms without labeled data, proposing a meta-learning approach called HPOD that transfers prior performance from benchmark datasets to new datasets, resulting in average performance improvements of 58% and 66% over default hyperparameters for LOF and iForest.
Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without any labels? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the first systematic approach called HPOD that is based on meta-learning. HPOD capitalizes on the prior performance of a large collection of HPs on existing OD benchmark datasets, and transfers this information to enable HP evaluation on a new dataset without labels. Moreover, HPOD adapts a prominent sampling paradigm to identify promising HPs efficiently. Extensive experiments show that HPOD works with both deep (e.g., Robust AutoEncoder) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (iForest)) OD algorithms on discrete and continuous HP spaces, and outperforms a wide range of baselines with on average 58% and 66% performance improvement over the default HPs of LOF and iForest.