The kernel Fisher discriminant analysis-order regression machine multi-class classification modeling method based on time point partition
基于时点分割的核Fisher判别分析–顺序回归机多类分类建模方法

To tackle the multi-class classification problem of multidimensional time series, we present a kernel Fisher discriminant analysis (KFDA) and order regression machine (ORM) multi-class classification model based on the time point partition. The classification decision function of this method is obtained from the complementarities of KFDA and ORM. The time point segmentation is used to deal with the multidimensional time series of the samples. The classification level at each time point is determined by the decision function. The final classification results of the samples in the sampling period are obtained by using the exponential smoothing. The experimental results show that the algorithm has desirable results in the classification of multi-dimensional time series, and provides an effective multi-class classification model.