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The detail of the training phase is explained as below: Generating randomly subsets Si from the original dataset S by sampling with replacement; Training those subsets with decision tree model; Calculating the average prediction result for each new subset.
After training the Random Forest model, to identify the class labels of the flying targets, we conduct extracting features of the flying targets and traversing them through each decision tree of the Random Forest. Each decision tree will give a vote for a class label with the input data are 84-dimensional vectors. Aggregating the vote on each decision tree, we finalize the class label of the flying target based on majority voting. The class label which received the most votes is the class label of the flying target. In case there are class labels that received the same number of votes, we choose one label randomly. |
IEEE/ICACT20220272 Slide.13
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