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Shallow and ensemble deep randomized neural network for anomaly detection.

Anuradha Kumari¹, A K Malik¹, M Tanveer¹

¹Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India.

Neural Networks : the Official Journal of the International Neural Network Society

|November 7, 2025

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We introduce the one-class ensemble deep RVFL (OC-edRVFL), an advanced anomaly detection model. This novel approach enhances stability and generalization for large datasets, outperforming traditional methods.

Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

Anomaly detection, or one-class classification (OCC), is crucial for real-world applications.
Traditional support vector machine-based OCC models struggle with large datasets and kernel sensitivity.
Existing models often have limitations in capturing complex patterns due to single hidden layers.

Purpose of the Study:

To propose novel deep learning models for enhanced anomaly detection.
To overcome the limitations of traditional OCC methods, particularly with large-scale datasets.
To improve the generalization, stability, and robustness of one-class classification models.

Main Methods:

Introduction of the one-class random vector functional link (OC-RVFL) network, fusing linear and nonlinear patterns.

Keywords:

Deep learning Ensemble deep RVFL Ensemble learning Neural networks with multiple output layers One-class classification Random vector functional link (RVFL) network

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Development of the one-class ensemble deep RVFL (OC-edRVFL) by integrating deep learning and ensemble learning with OC-RVFL.

Utilizing a closed-form solution for efficient output weight computation and deriving generalization error bounds.

Main Results:

The OC-edRVFL model demonstrates superior stability, robustness, and generalization compared to the OC-RVFL.
Experiments on diverse datasets (artificial, UCI, NDC, MNIST) show OC-edRVFL outperforms baseline models.
The proposed models exhibit high performance on datasets with up to 5 million samples.

Conclusions:

The OC-edRVFL is a highly effective and scalable solution for anomaly detection.
The novel deep ensemble approach significantly advances the capabilities of one-class classification.
The models offer reduced training time and improved performance on large, complex datasets.