论文标题
功能标记的最佳分区
Functional Labeled Optimal Partitioning
论文作者
论文摘要
峰检测是顺序数据分析中的一个问题，涉及区分较高计数（峰）与计数较低区域（背景噪声）的区域。 在火车和测试集的标签集中，正确预测偏离背景噪声的区域至关重要。 已经提出了动态编程更改点算法来通过限制平均值来替代增加然后减小来解决峰值检测问题。 当前受约束的更改点算法仅在测试集上创建预测，同时完全忽略了火车集。 拟合火车集合时都准确的变更点算法，并在测试集上进行预测，但在峰值检测模型的背景下没有提出。 我们建议通过创建一种新的动态编程算法Flopart来解决这些问题，该算法的火车标签错误，并能够在测试集上提供高度准确的预测。 我们提供的经验分析表明，在火车和测试标签错误方面，Flopart具有相似的时间复杂性，同时比现有算法更准确。
Peak detection is a problem in sequential data analysis that involves differentiating regions with higher counts (peaks) from regions with lower counts (background noise). It is crucial to correctly predict areas that deviate from the background noise, in both the train and test sets of labels. Dynamic programming changepoint algorithms have been proposed to solve the peak detection problem by constraining the mean to alternatively increase and then decrease. The current constrained changepoint algorithms only create predictions on the test set, while completely ignoring the train set. Changepoint algorithms that are both accurate when fitting the train set, and make predictions on the test set, have been proposed but not in the context of peak detection models. We propose to resolve these issues by creating a new dynamic programming algorithm, FLOPART, that has zero train label errors, and is able to provide highly accurate predictions on the test set. We provide an empirical analysis that shows FLOPART has a similar time complexity while being more accurate than the existing algorithms in terms of train and test label errors.
