Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
This work addresses the issue of limited robustness in autoencoder-based outlier detection for unsupervised learning scenarios, offering an incremental improvement over existing ensemble methods.
The paper tackles the problem of overfitting in unsupervised outlier detection with autoencoders by proposing a boosting-based ensemble method (BAE) that sequentially trains autoencoders with weighted sampling to reduce outlier influence and inject diversity, resulting in improved performance that outperforms state-of-the-art approaches under various conditions.
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.