论文标题
有监督的特征子集选择和多元时间序列的特征排名无功能提取
Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction
论文作者
论文摘要
我们介绍了多元时间序列（MTS）分类的监督功能排名和特征子集选择算法。与大多数现有的监督/无监督/无监督的特征选择算法不同，我们的技术不需要功能提取步骤来生成时间序列中的一维功能向量。取而代之的是，它基于直接计算单个时间序列之间的相似性并评估产生的群集结构与标签的匹配程度。这些技术适合异质MTS数据，其中时间序列测量可能具有不同的采样分辨率，并且与多模式数据有关。
We introduce supervised feature ranking and feature subset selection algorithms for multivariate time series (MTS) classification. Unlike most existing supervised/unsupervised feature selection algorithms for MTS our techniques do not require a feature extraction step to generate a one-dimensional feature vector from the time series. Instead it is based on directly computing similarity between individual time series and assessing how well the resulting cluster structure matches the labels. The techniques are amenable to heterogeneous MTS data, where the time series measurements may have different sampling resolutions, and to multi-modal data.
