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Posterior probability algorithm based on weighted decision tree

A posterior probability and decision tree technology, applied in the direction of calculation, calculation model, special data processing application, etc., can solve problems such as inapplicability, increase reliability, reduce uneven distribution of data samples, and reduce overfitting. Effect

Inactive Publication Date: 2020-05-15
GUIYANG SHIJIHENGTONG TECH
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AI Technical Summary

Problems solved by technology

Decision tree, as a supervised machine learning algorithm for calculating expected value, is widely used in various predictive analysis. The traditional decision tree algorithm calculates the entropy value of nodes in the case of equal probability as the prediction result, but when encountering Some special situations that require additional weights may not be applicable

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  • Posterior probability algorithm based on weighted decision tree
  • Posterior probability algorithm based on weighted decision tree
  • Posterior probability algorithm based on weighted decision tree

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Embodiment Construction

[0027] The present invention will be further explained below in conjunction with the accompanying drawings and specific embodiments.

[0028] like figure 1 As shown, a decision tree-based posterior probability algorithm, the method is based on the conventional decision tree algorithm, and when the decision tree is split, the weight factor is added to improve the influence of the confidence classification, thus strengthening the high confidence The final information entropy of the node, the changed information entropy will effectively change the overall probability distribution relationship, and untrustworthy classification will reduce its own influence;

[0029] The specific steps of the decision tree-based posterior probability algorithm are as follows:

[0030] The classification of data is converted into numerical value, and the processed data is used as the data source, and each classification situation is iteratively iterated, and the best information gain is calculated ...

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Abstract

The invention discloses a posterior probability algorithm based on a weighted decision tree. According to the method, based on a conventional decision tree algorithm, a weight influence factor is added when a decision tree is split, and the influence of signal-to-noise ratio classification is improved, so the final information entropy of nodes with high signal-to-noise ratio is enhanced, the overall probability distribution relation is effectively changed by the changed information entropy, and the influence of untrusted classification is reduced. According to the invention, the error influence caused by abnormal data can be effectively reduced; the dual polarization phenomenon caused by unbalanced data sample distribution is reduced; the influence of credibility is brought in, so the reliability of the model is improved; and an over-fitting phenomenon caused by data exception is reduced.

Description

technical field [0001] The invention belongs to the posterior probability algorithm based on weighted decision tree, relates to the field of data structure, and is especially applicable to the method in the field of data mining Background technique [0002] A decision tree is the process of classifying data through a set of rules. It provides a rule-like way of getting what value under what conditions. Decision trees are divided into classification trees and regression trees. The classification tree is a decision tree for discrete variables, and the regression tree is a decision tree for continuous variables. [0003] Recent surveys show that decision tree is also the most frequently used data mining algorithm, and its concept is very simple. A very important reason why the decision tree algorithm is so popular is that users basically do not need to understand the machine learning algorithm, nor do they need to delve into how it works. Intuitively, a decision tree classif...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/26G06N20/00
CPCG06F16/2465G06F16/26G06N20/00
Inventor 杨兴荣胡勇杨兴海廖毅朱恒刘洋邓孔祥王芳王龙漆国强刘冬洋
Owner GUIYANG SHIJIHENGTONG TECH
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