Integrated pruning strategy based on balanced binary tree

A balanced binary tree and pruning technology, applied in the field of integrated learning, can solve the problems of difficult to eliminate the base classifier of test accuracy and low generalization performance, achieve the best generalization performance, improve integration accuracy, and reduce the dependence on computer hardware resources Effect

Active Publication Date: 2020-09-04
CENT SOUTH UNIV
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Problems solved by technology

[0005] The present invention provides an integrated pruning strategy based on a balanced binary tree, the purpose of which is to solve the problems in the background technology th

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  • Integrated pruning strategy based on balanced binary tree
  • Integrated pruning strategy based on balanced binary tree
  • Integrated pruning strategy based on balanced binary tree

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[0024] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0025] Aiming at existing problems, the present invention provides a kind of integrated pruning strategy based on balanced binary tree, comprising the following steps:

[0026] S1. Base classifier integration pool initialization: segment the large data set to form many sub-data sets, and then perform training and testing for each sub-data set to form an initial complete classifier pool; the big data includes normal conditions The data set under and the data set in the abnormal case. The ratio of the number of data sets under normal conditions to the data sets under abnormal conditions ranges from 100:1 to 1000:1.

[0027] The segmentation work is as follows: segment the data set under normal conditions to obtain sub-data sets under normal conditions, an...

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Abstract

The invention provides an integrated pruning strategy based on a balanced binary tree, and the structure of a multi-step multi-line ship lock comprises the following steps: S1, initializing a base classifier integrated pool: segmenting a big data set to form a plurality of sub-data sets, and carrying out the training and testing of each sub-data set, thereby forming an initial complete classifierpool; S2, constructing a balanced binary tree to form a final sub-integration; and S3, predicting and classifying new data samples by using the reserved optimal sub-ensemble. According to the method,the technical problems that a fitting phenomenon is easy to generate, a base classifier with too high or too low test precision is difficult to remove, and the generalization performance is not high are solved.

Description

technical field [0001] The invention relates to the technical field of integrated learning, in particular to an integrated pruning strategy based on a balanced binary tree. Background technique [0002] Integrated learning solves many problems faced by single classifiers in the process of massive data training and learning. However, since integrated learning consists of multiple single classifiers to form an integrated pool to complete prediction or classification tasks, it poses a higher requirement for computer hardware resources. Requirements, a common way to solve this problem is to use the idea of ​​integrated pruning strategy to reduce the number of single classifiers used as much as possible while ensuring that the final prediction or classification accuracy of integrated learning is not reduced or even improved. [0003] The current popular ensemble pruning strategy is the ensemble pruning strategy based on the clustering algorithm. This method uses the test accuracy...

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Application Information

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IPC IPC(8): G06N3/08G06N20/20
CPCG06N3/082G06N20/20
Inventor 邓晓衡蔚永黑聪刘梦杰
Owner CENT SOUTH UNIV
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