Optimized classification method and optimized classification device based on random forest algorithm

Abstract

The invention relates to an optimized classification method and an optimized classification device based on a random forest algorithm. The optimized classification method comprises the following steps of a first step, dividing given sample data into k sub-training sets which are independent from one another, selecting different decision tress according to each training sub-set, selecting different decision attributes by the decision trees for forming base classifiers, and forming a random forest by the base classifiers; a second step, in each base classifier, distributing a preset weight to each set, then transmitting to-be-classified data into the random forest which is constructed in the step 1) for performing classification, and adjusting the weight according to a classification result and a predication result, if the classification predication result of the set does not accord with the actual result, increasing the weight of the set, and if the classification predication result accords with the actual result, reducing the weight of the set; and a third step, performing classification on the to-be-classified data according to the adjusted weight of each set until the classification result accords with the predication result.

Description

technical field [0001] The invention relates to an optimization classification method and device based on a random forest algorithm. Background technique [0002] In 2001, Leo Breima proposed a classification model based on decision tree theory: Random Forests (RF) algorithm. The random forest algorithm is a combined classifier composed of multiple decision trees, which significantly improves the classification accuracy compared with a single decision tree. The random forest classification algorithm can be regarded as a forest composed of many trees. All trees participate in voting to determine the final classification result. The growth of each tree is determined by the random variable introduced, that is, random selection of split attributes and random selection of training samples. Generate a decision tree, all trees participate in voting, and then summarize the classification results. Random forest improves the prediction accuracy without significantly increasing the a...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an optimized classification method based on the random forest algorithm to solve the calculation problem that the classification performance accuracy of unbalanced data is not high in the traditional random forest classification method

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