Controllable RANSAC-based Anomaly Detection via Hypothesis Testing

TL;DR

This paper introduces CTRL-RANSAC, a statistical method that enhances RANSAC-based anomaly detection by controlling false positive rates and improving detection reliability through hypothesis testing and selective inference.

Contribution

It presents a novel controllable RANSAC framework that guarantees a pre-specified error level in anomaly detection, combining hypothesis testing with RANSAC for more reliable results.

Findings

CTRL-RANSAC effectively controls the false positive rate below the specified level.

The method improves true detection rates compared to traditional RANSAC.

Experimental results on synthetic and real datasets validate the theoretical guarantees.

Abstract

Detecting the presence of anomalies in regression models is a crucial task in machine learning, as anomalies can significantly impact the accuracy and reliability of predictions. Random Sample Consensus (RANSAC) is one of the most popular robust regression methods for addressing this challenge. However, this method lacks the capability to guarantee the reliability of the anomaly detection (AD) results. In this paper, we propose a novel statistical method for testing the AD results obtained by RANSAC, named CTRL-RANSAC (controllable RANSAC). The key strength of the proposed method lies in its ability to control the probability of misidentifying anomalies below a pre-specified level $\alpha$ (e.g., $\alpha = 0.05$). By examining the selection strategy of RANSAC and leveraging the Selective Inference (SI) framework, we prove that achieving controllable RANSAC is indeed feasible. Furthermore, we introduce a more strategic and computationally efficient approach to enhance the true detection rate and overall performance of the CTRL-RANSAC. Experiments conducted on synthetic and real-world datasets robustly support our theoretical results, showcasing the superior performance of the proposed method.