通过强制模型学不同特征，提升时间序列分类的集成效果。
Enhancing Time Series Classification with Diversity-Driven Neural Network Ensembles
- 用特征正交性损失让每个模型学不同特征，避免重复。
- 在128个数据集上用更少模型达到顶尖性能。
- 适合追求高效高精度时间序列分类的研究者。
集成方法在多种机器学习任务中表现优异，主要得益于各模型学习到的特征多样性。在时间序列分类（TSC）中，基于神经网络的集成方法虽有效，但多数现有方法使用相同架构训练多个模型，未显式促进多样性，导致特征冗余，削弱了集成优势。本文提出一种驱动多样性的集成学习框架，通过在学习到的特征表示上施加特征正交性损失，实现模型间特征的去相关。该策略确保各模型捕捉互补信息而非重复内容。我们在UCR时间序列归档的128个数据集上评估该框架，结果表明，仅用较少模型即可实现当前最优（SOTA）性能，相较传统神经网络集成方法更具效率与可扩展性。
原文摘要 · Abstract (English)
Ensemble methods have played a crucial role in achieving state-of-the-art (SOTA) performance across various machine learning tasks by leveraging the diversity of features learned by individual models. In Time Series Classification (TSC), ensembles have proven highly effective whether based on neural networks (NNs) or traditional methods like HIVE-COTE. However most existing NN-based ensemble methods for TSC train multiple models with identical architectures and configurations. These ensembles aggregate predictions without explicitly promoting diversity which often leads to redundant feature representations and limits the benefits of ensembling. In this work, we introduce a diversity-driven ensemble learning framework that explicitly encourages feature diversity among neural network ensemble members. Our approach employs a decorrelated learning strategy using a feature orthogonality loss applied directly to the learned feature representations. This ensures that each model in the ensemble captures complementary rather than redundant information. We evaluate our framework on 128 datasets from the UCR archive and show that it achieves SOTA performance with fewer models. This makes our method both efficient and scalable compared to conventional NN-based ensemble approaches.
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