HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection

Yue Zhao, Leman Akoglu
Proceedings of the Third International Conference on Automated Machine Learning, PMLR 256:2/1-24, 2024.

Abstract

Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without using any labels? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the first continuous HP search method called HPOD. It 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. Also, HPOD adapts a prominent, (originally) supervised, sampling paradigm to efficiently identify promising HPs in iterations. Extensive experiments show that HPOD works for both deep (e.g., Robust AutoEncoder (RAE)) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (Forest)) algorithms on discrete and continuous HP spaces. HPOD outperforms a wide range of diverse baselines with 37% improvement on average over the minimal loss HPs of RAE, and 58% and 66% improvement on average over the default HPs of LOF and iForest.

Cite this Paper

BibTeX
@InProceedings{pmlr-v256-zhao24a, title = {HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection}, author = {Zhao, Yue and Akoglu, Leman}, booktitle = {Proceedings of the Third International Conference on Automated Machine Learning}, pages = {2/1--24}, year = {2024}, editor = {Eggensperger, Katharina and Garnett, Roman and Vanschoren, Joaquin and Lindauer, Marius and Gardner, Jacob R.}, volume = {256}, series = {Proceedings of Machine Learning Research}, month = {09--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v256/main/assets/zhao24a/zhao24a.pdf}, url = {https://proceedings.mlr.press/v256/zhao24a.html}, abstract = {Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without using any labels? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the first continuous HP search method called HPOD. It 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. Also, HPOD adapts a prominent, (originally) supervised, sampling paradigm to efficiently identify promising HPs in iterations. Extensive experiments show that HPOD works for both deep (e.g., Robust AutoEncoder (RAE)) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (Forest)) algorithms on discrete and continuous HP spaces. HPOD outperforms a wide range of diverse baselines with 37% improvement on average over the minimal loss HPs of RAE, and 58% and 66% improvement on average over the default HPs of LOF and iForest.} }
Endnote
%0 Conference Paper %T HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection %A Yue Zhao %A Leman Akoglu %B Proceedings of the Third International Conference on Automated Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Katharina Eggensperger %E Roman Garnett %E Joaquin Vanschoren %E Marius Lindauer %E Jacob R. Gardner %F pmlr-v256-zhao24a %I PMLR %P 2/1--24 %U https://proceedings.mlr.press/v256/zhao24a.html %V 256 %X Given an unsupervised outlier detection (OD) algorithm, how can we optimize its hyperparameter(s) (HP) on a new dataset, without using any labels? In this work, we address this challenging hyperparameter optimization for unsupervised OD problem, and propose the first continuous HP search method called HPOD. It 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. Also, HPOD adapts a prominent, (originally) supervised, sampling paradigm to efficiently identify promising HPs in iterations. Extensive experiments show that HPOD works for both deep (e.g., Robust AutoEncoder (RAE)) and shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (Forest)) algorithms on discrete and continuous HP spaces. HPOD outperforms a wide range of diverse baselines with 37% improvement on average over the minimal loss HPs of RAE, and 58% and 66% improvement on average over the default HPs of LOF and iForest.
APA
Zhao, Y. & Akoglu, L.. (2024). HPOD: Hyperparameter Optimization for Unsupervised Outlier Detection. Proceedings of the Third International Conference on Automated Machine Learning, in Proceedings of Machine Learning Research 256:2/1-24 Available from https://proceedings.mlr.press/v256/zhao24a.html.

Related Material