Time series classification is essential in many fields, such as medicine,
finance, environmental science, and manufacturing, enabling tasks like disease
diagnosis, anomaly detection, and stock price prediction. Machine learning
models like Recurrent Neural Networks and InceptionTime, while successful in
numerous applications, can face scalability limitations due to intensive
training requirements. To address this, random convolutional kernel models such
as Rocket and its derivatives have emerged, simplifying training and achieving
state-of-the-art performance by utilizing a large number of randomly generated
features from time series data. However, due to their random nature, most of
the generated features are redundant or non-informative, adding unnecessary
computational load and compromising generalization. Here, we introduce
Sequential Feature Detachment (SFD) as a method to identify and prune these
non-essential features. SFD uses model coefficients to estimate feature
importance and, unlike previous algorithms, can handle large feature sets
without the need for complex hyperparameter tuning. Testing on the UCR archive
demonstrates that SFD can produce models with $10\%$ of the original features
while improving $0.2\%$ the accuracy on the test set. We also present an
end-to-end procedure for determining an optimal balance between the number of
features and model accuracy, called Detach-ROCKET. When applied to the largest
binary UCR dataset, Detach-ROCKET is capable of reduce model size by $98.9\%$
and increases test accuracy by $0.6\%$.

通过引入顺序特征分离（SFD）作为一种方法来识别和修剪这些非必需特征，本研究解决了时间序列数据中大量随机生成特征的冗余性和非信息性的问题，通过测试在 UCR 存档上表明 SFD 可以在保持高准确率的同时，将模型特征数量减少到原始数量的 10％，并提供了一种称为 Detach-ROCKET 的端到端过程以在模型准确性和特征数量之间找到最佳平衡。