Random forest optimization method and system based on tensor decomposition

Abstract

The invention is applicable to the data mining field, and provides a random forest optimization method based on tensor decomposition. The random forest optimization method comprises the steps of: reading a training data set as an initial training set; acquiring a new training set based on the initial training set by adopting a preset random sampling method to form a random forest training set, wherein the random forest training set comprises a decision-making tree model trained by adopting a random sub-space technique; constructing a tensor model based o the decision-making tree model in the random forest training set; decomposing the tensor model by adopting a preset tensor decomposition technique; adjusting decomposed tensors to obtain adjusted tensors; selecting a decision-making tree subset with overall optimization from the adjusted tensors as an optimal base classifier subset; and utilizing a test sample set for testing the optimal base classifier subset. The invention further provides a Random forest optimization system based on tensor decomposition. The Random forest optimization method and the Random forest optimization system can obtain the random forest subset with the optimal overall performance.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a random forest optimization method and system based on tensor decomposition. Background technique [0002] The current clustering-based ensemble learning machine optimization method needs to measure the similarity of the prediction results of two base classifiers and the diversity between them, these metrics play a crucial role in the optimization process of ensemble learning machines, however Diversity is difficult to measure in practice, and an effective link between diversity and the predictive performance of ensemble learning machines is also difficult to establish. [0003] Random forest has received extensive attention in the machine learning community because it can significantly improve the generalization ability of a learning system. Use random sampling techniques to obtain multiple samples with diversity differences, and train multiple sets of decision trees on these self-s...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the purpose of the embodiment of the present invention is to provide a random forest optimization method and system based on tensor decomposition, aiming to solve the problem of low prediction efficiency in the machine learning field in the prior art

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