# RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets

arXiv: https://arxiv.org/abs/2510.06278 · HTML: https://mlsanity.com/paper/2510.06278/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Associative Long Short-Term Memory](https://mlsanity.com/paper/1602.03032/) (2016)
- [Random vector functional link network: recent developments, applications, and future directions](https://mlsanity.com/paper/2203.11316/) (2022)
- [Neuronal Synchrony in Complex-Valued Deep Networks](https://mlsanity.com/paper/1312.6115/) (2013)
- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning](https://mlsanity.com/paper/2307.07881/) (2023)
- [GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing](https://mlsanity.com/paper/2409.16735/) (2024)
- [Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern](https://mlsanity.com/paper/2405.07070/) (2024)
- [Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System](https://mlsanity.com/paper/2406.00801/) (2024)

## Built on by (most cited first, 2 of 2)

- [Robust Dual-Model Collaborative Random Vector Functional Link Network](https://mlsanity.com/paper/2608.13628/) (2026)
- [ECA-BLS: An Efficient Complex-Augmented Broad Learning System](https://mlsanity.com/paper/2608.29763/) (2026)

## Abstract (arXiv)

Recent advancements in neural networks, supported by foundational theoretical insights, emphasize the superior representational power of complex numbers. However, their adoption in randomized neural networks (RNNs) has been limited due to the lack of effective methods for transforming real-valued tabular datasets into complex-valued representations. To address this limitation, we propose two methods for generating complex-valued representations from real-valued datasets: a natural transformation and an autoencoder-driven method. Building on these mechanisms, we propose RVFL-X, a complex-valued extension of the random vector functional link (RVFL) network. RVFL-X integrates complex transformations into real-valued datasets while maintaining the simplicity and efficiency of the original RVFL architecture. By leveraging complex components such as input, weights, and activation functions, RVFL-X processes complex representations and produces real-valued outputs. Comprehensive evaluations on 80 real-valued UCI datasets demonstrate that RVFL-X consistently outperforms both the original RVFL and state-of-the-art (SOTA) RNN variants, showcasing its robustness and effectiveness across diverse application domains.