用浅层神经网络替代决策树，提升结构化与非结构化数据的预测精度。
Multiple Additive Neural Networks for Structured and Unstructured Data
- 以CNN和胶囊网络为基学习器，取代传统决策树构建增强型梯度提升模型。
- 在多个公开数据集上优于XGB等传统方法，准确率显著提升。
- 适合需要高泛化能力的复杂数据场景，如图像、音频与多源数据融合任务。
本文拓展并解释了多重加性神经网络（MANN）方法，作为传统梯度提升框架的改进，采用近似浅层神经网络而非决策树作为基学习器。该创新方法利用卷积神经网络（CNN）和胶囊神经网络架构，将MANN的应用范围扩展至结构化数据与非结构化数据（如图像、音频）。对于结构化数据，利用胶囊神经网络作为特征提取器，并与MANN结合构成分类器。MANN独特的架构支持持续学习，集成先进启发式策略以抑制过拟合，增强鲁棒性并降低对学习率、迭代次数等超参数的敏感性。实证研究显示，MANN在多个知名数据集上的准确率超越极端梯度提升（XGB）等传统方法。本研究证明MANN具有更优的精度与泛化能力，是一种适用于多样化数据类型与复杂学习环境的通用工具。
原文摘要 · Abstract (English)
This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework, utilizing nearly shallow neural networks instead of decision trees as base learners. This innovative approach leverages neural network architectures, notably Convolutional Neural Networks (CNNs) and Capsule Neural Networks, to extend its application to both structured data and unstructured data such as images and audio. For structured data the advantages of capsule neural networks as feature extractors are used and combined with MANN as a classifier. MANN's unique architecture promotes continuous learning and integrates advanced heuristics to combat overfitting, ensuring robustness and reducing sensitivity to hyperparameter settings like learning rate and iterations. Our empirical studies reveal that MANN surpasses traditional methods such as Extreme Gradient Boosting (XGB) in accuracy across well-known datasets. This research demonstrates MANN's superior precision and generalizability, making it a versatile tool for diverse data types and complex learning environments.
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