将实值表格数据转为复数表示，提升随机网络性能
RVFL-X: A Novel Randomized Network Based on Complex Transformed Real-Valued Tabular Datasets
- 用自然变换和自编码器生成数据的复数表示
- 在80个UCI数据集上超越原版RVFL和主流随机网络
- 适合需要高效高精度建模的表格数据场景
近期神经网络的发展表明复数在表示能力上具有优势，但受限于缺乏有效的实值表格数据到复数表示的转换方法，复数在随机神经网络中的应用仍有限。为此，本文提出两种生成复数表示的方法：自然变换与自编码器驱动方法。基于此，提出RVFL-X，即随机向量函数链接网络的复数扩展。该模型在保持原始RVFL简洁高效的基础上，引入输入、权重和激活函数的复数形式，处理复数表示并输出实值结果。在80个真实世界UCI数据集上的全面评估表明，RVFL-X始终优于原始RVFL及当前最先进的随机网络变体，在多种应用场景中展现出稳健性和有效性。
原文摘要 · Abstract (English)
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.
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