Multi-period classification: learning sequent classes from temporal domains
Multi-period classification: learning sequent classes from temporal domains
复制标题
DOI：
10.1007/s10618-014-0376-8
复制
发表时间：
2015-05-01
影响因子：
4.8
通讯作者：
Antunes, Claudia
中科院分区：
文献类型：
--
作者：
Henriques, Rui;Madeira, Sara C.;Antunes, Claudia
随着现实世界中的大多数决策随着时间的推移而变化，扩展传统的分类器来处理跨不同时间段对感兴趣的属性进行分类的问题变得越来越重要。解决这个问题，称为多阶段分类，对于回答现实世界的任务至关重要，例如预测即将到来的医疗保健需求或行政规划任务。在这种情况下，虽然现有的研究提供了从复杂数据域中学习单个标签的原理，但对类的学习序列(符号时间序列)的问题关注较少。这项工作激发了对多周期分类器的需求，并提出了一种基于聚类的多周期分类方法(CMPC)，该方法保留了被分类周期之间的局部依赖关系。对真实数据集的评估提供了多周期分类器相关性的证据，并显示了CMPC方法相对于适用于具有大量周期的多周期任务的长期预测的同级方法的优越性能。
As the majority of real-world decisions change over time, extending traditional classifiers to deal with the problem of classifying an attribute of interest across different time periods becomes increasingly important. Tackling this problem, referred to as multi-period classification, is critical to answer real-world tasks, such as the prediction of upcoming healthcare needs or administrative planning tasks. In this context, although existing research provides principles for learning single labels from complex data domains, less attention has been given to the problem of learning sequences of classes (symbolic time series). This work motivates the need for multi-period classifiers, and proposes a method, cluster-based multi-period classification (CMPC), that preserves local dependencies across the periods under classification. Evaluation against real-world datasets provides evidence of the relevance of multi-period classifiers, and shows the superior performance of the CMPC method against peer methods adapted from long-term prediction for multi-period tasks with a high number of periods.