Multi-view learning algorithm based on random forest

Abstract

Random forests are one of the most classical machine learning algorithms and have been widely applied. However, observation finds that although numerous multi-view data exist in reality and wide analytical studies have been obtained, it is surprising that random forest construction for a multi-view scene is very few. Only methods for solving the multi-view learning problem by using random forestsare used for generating respective random forests for each view, and then multi-view information is fused during decision making. One significant defect of the method is that the correlation among multiple views is not utilized in the construction stage of the random forest, so that information resources are undoubtedly wasted. In order to make up the defect, the invention provides an improved multi-view learning algorithm based on random forest. Specifically, view fusion is carried out in the generation process of decision trees, information interaction between views is fused into the construction stage of the decision trees, and utilization of complementary information between the views in the whole random forest generation process is achieved. In addition, decision boundaries with discrimination properties are generated for the decision tree through discriminant analysis, so that the algorithm is more suitable for classification.

Description

technical field [0001] The invention belongs to the field of machine learning, and in particular relates to a random forest-based multi-view learning method, which is used to solve classification problems in multi-view scenes. Background technique [0002] Random forest, first proposed by Breiman in 2001, has become one of the most widely used ensemble learning algorithms. Random forest constructs multiple decision trees by using random resampling and node random splitting strategy, and then obtains the final classification result by voting. Due to its advantages of high precision, good interpretability, low overfitting risk and good noise tolerance, it has achieved impressive success in many fields including computer vision and data mining, and has also inspired many successors. Based on extensive research on random forests, researchers have developed variant random forests such as dynamic random forests and deep forests. [0003] Nevertheless, existing random forests and...

AI Technical Summary

Problems solved by technology

It can be seen that these methods do not make full use of the correlation between multi-views in the construction stage of the random forest, which is undoubtedly a waste of information resources.

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