Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection

TL;DR

This paper introduces BAE, an unsupervised boosting-based autoencoder ensemble that enhances outlier detection by increasing diversity and robustness, outperforming existing methods across various scenarios.

Contribution

The paper proposes a novel boosting-based ensemble of autoencoders that adaptively builds a cascade to improve outlier detection performance in an unsupervised setting.

Findings

BAE outperforms state-of-the-art outlier detection methods.

The ensemble reduces overfitting through adaptive weighted sampling.

Experimental results demonstrate robustness across multiple datasets.

Abstract

Autoencoders, as a dimensionality reduction technique, have been recently applied to outlier detection. However, neural networks are known to be vulnerable to overfitting, and therefore have limited potential in the unsupervised outlier detection setting. Current approaches to ensemble-based autoencoders do not generate a sufficient level of diversity to avoid the overfitting issue. To overcome the aforementioned limitations we develop a Boosting-based Autoencoder Ensemble approach (in short, BAE). BAE is an unsupervised ensemble method that, similarly to the boosting approach, builds an adaptive cascade of autoencoders to achieve improved and robust results. BAE trains the autoencoder components sequentially by performing a weighted sampling of the data, aimed at reducing the amount of outliers used during training, and at injecting diversity in the ensemble. We perform extensive experiments and show that the proposed methodology outperforms state-of-the-art approaches under a variety of conditions.