Research on Comparisons with three survival models Comparisons with Decision Tree, Random forests and CNNs

Abstract

Decision tree, random forests and CNN are three advanced algorithms that can solve classification tasks. This article will compare the performance of the three different algorithms in classification tasks. Here, CNN extracts features through convolution operations to detect objects or recognize images, other two achieve this through building a tree structure. They could help to solve so many problems. Decision tree have been widely applied in fields such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. CNN can be used to detect individual plants or pixels as well as for vegetation classification. Nevertheless, they still encounter challenges in the classification tasks, such as low classification accuracy.