# Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection

arXiv: https://arxiv.org/abs/1902.00567 · HTML: https://mlsanity.com/paper/1902.00567/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection](https://mlsanity.com/paper/2205.09884/) (2022)
- [SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection](https://mlsanity.com/paper/2302.08618/) (2023)
- [Time Series Anomaly Detection with label-free Model Selection](https://mlsanity.com/paper/2106.07473/) (2021)
- [UniNet: A Unified Multi-granular Traffic Modeling Framework for Network Security](https://mlsanity.com/paper/2503.04174/) (2025)
- [Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2501.14694/) (2025)

## Abstract (arXiv)

In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. This is a common goal in predictive maintenance, which aims to forecast the imminent faults of an appliance given abundant samples of normal behaviors. Local outlier factor (LOF) is one of the state-of-the-art models used for anomaly detection, but the predictive performance of LOF depends greatly on the selection of hyperparameters. In this paper, we propose a novel, heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model that uses the proposed method shows good predictive performance in both simulations and real data sets.