提出一种基于累积直方图差异的火山喷发数据分割新方法，提升模型学习效果。
Benchmarking of a new data splitting method on volcanic eruption data
- 用累积直方图差异指数迭代划分火山喷发数据集，确保两部分差异最大化。
- 相比随机与K均值分割，该方法训练更久且未触发早停，表明学习更充分。
- 适合需要高质量数据划分的地质灾害预测研究者使用。
本文提出一种新的数据分割方法：通过累积直方图差异（CHD）指数，对火山喷发数据集进行迭代划分，将其分为两个部分。实验结果显示，该方法在不同配置下的表现优于随机分割和K-means分割，虽所需训练轮次略多，但因未触发早停机制，说明模型未出现过拟合，学习过程更为深入。这表明分割质量直接影响模型性能，尤其在需要充分挖掘数据特征的任务中具有优势。
原文摘要 · Abstract (English)
In this paper, a novel method for data splitting is presented: an iterative procedure divides the input dataset of volcanic eruption, chosen as the proposed use case, into two parts using a dissimilarity index calculated on the cumulative histograms of these two parts. The Cumulative Histogram Dissimilarity (CHD) index is introduced as part of the design. Based on the obtained results the proposed model in this case, compared to both Random splitting and K-means implemented over different configurations, achieves the best performance, with a slightly higher number of epochs. However, this demonstrates that the model can learn more deeply from the input dataset, which is attributable to the quality of the splitting. In fact, each model was trained with early stopping, suitable in case of overfitting, and the higher number of epochs in the proposed method demonstrates that early stopping did not detect overfitting, and consequently, the learning was optimal.
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