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

## TL;DR

This paper introduces a heuristic method for automatically tuning hyperparameters of the Local Outlier Factor model, significantly improving anomaly detection performance in various applications.

## Contribution

A novel heuristic approach for hyperparameter tuning in LOF that enhances its predictive accuracy in anomaly detection tasks.

## Key findings

- Tuned LOF outperforms default settings in simulations.
- Method improves anomaly detection accuracy on real datasets.
- Tuning method is practical and adaptable to different scenarios.

## Abstract

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.