# Deep Stacked Stochastic Configuration Networks for Lifelong Learning of Non-Stationary Data Streams

arXiv: https://arxiv.org/abs/1808.02234 · HTML: https://mlsanity.com/paper/1808.02234/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis](https://mlsanity.com/paper/2104.09396/) (2021)
- [Advancing continual lifelong learning in neural information retrieval: definition, dataset, framework, and empirical evaluation](https://mlsanity.com/paper/2308.08378/) (2023)
- [Automatic Learning to Detect Concept Drift](https://mlsanity.com/paper/2105.01419/) (2021)
- [Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams](https://mlsanity.com/paper/2107.02943/) (2021)
- [Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks](https://mlsanity.com/paper/2307.00507/) (2023)
- [Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modelling](https://mlsanity.com/paper/2410.10072/) (2024)

## Abstract (arXiv)

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.