A novel feature reconstruction method, referred to as interval feature transformation (IFT), is proposed for time series classification. The IFT uses perceptually important points to segment the series dynamically into subsequences of unequal length, and then extract interval features from each time series subsequence as a feature vector. The IFT distinguishes the best top-k discriminative feature vectors from a data set by information gain. Utilizing these discriminative feature vectors, transformation is applied to generate new k-dimensional data which are lower-dimensional representations of the original data. In order to verify the effectiveness of this method, we use the transformed data in conjunction with some traditional classifiers to solve time series classification problems and make comparative experiments to several state-of-the-art algorithms. Experiment results verify the effectiveness, noise robustness and interpretability of the IFT.

中文翻译：

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使用感知重要点的时间序列分类的区间特征转换

提出了一种新的特征重建方法，称为间隔特征变换（IFT），用于时间序列分类。IFT利用感知上重要的点将序列动态分割成不等长的子序列，然后从每个时间序列子序列中提取区间特征作为特征向量。IFT 通过信息增益从数据集中区分最佳的 top-k 判别特征向量。利用这些判别特征向量，应用变换来生成新的 k 维数据，这些数据是原始数据的低维表示。为了验证该方法的有效性，我们将转换后的数据与一些传统分类器结合使用来解决时间序列分类问题，并对几种最先进的算法进行对比实验。实验结果验证了 IFT 的有效性、噪声鲁棒性和可解释性。