Weighted Deep Forest Algorithm Based on Dynamic Sliding Window
Deep forest is a typical machine learning method that is widely used in classification tasks.However,in time series classification,it is often easy to ignore the positive effect of time series change trend on its feature extraction.In addition,when the feature vectors in the cascade forest are updated,each subclassifier is treated equally,so that the classification ability of different submodels cannot be fully utilized,and finally the time series classification falls into the local optimum.In order to solve the above problems,we propose a weighted deep forest method based on dynamic sliding window,called AWGE-gcForest,for the classification of time series data.Firstly,according to the change trend of the time series,the AWGE-gcForest algorithm defines the window change value(WCV),which realizes the dynamic adjustment of the window and reduces the number of multi-granularity scans,so as to improve the efficiency of feature extraction,the accuracy of classification and the generalization ability.Secondly,the cascading forests are weighted by iterative optimum,and the forests with high classification accuracy are given more weight,so as to reduce the influence of the subtrees with weak classification on the whole model.The above operations consider the classification performance of the cascaded forest globally,and avoid falling into the local optimum,so as to reduce the number of cascading layers and reduce the time complexity.Compared with the TS-CHIEF algorithm,the MultiRocket algorithm,the DF21 algorithm and the OS-CNN algorithm on the UCR dataset,the classification accuracy and time efficiency of the proposed algorithm are better than those of the current advanced time series classification methods,and it is a relatively efficient time series classification algorithm.
time-series classificationdeep forestwindow change valueiterative optimumtrends of change
万方数据
维普
