Compressor Fault Classification Method Based on Balanced Binary Tree Integrated Pruning Strategy

A technology of balanced binary tree and fault classification, applied in the field of ensemble learning, can solve the problems of difficult to eliminate the test accuracy base classifier and low generalization performance, and achieve the effect of reducing the dependence of computer hardware resources, improving the integration accuracy and reducing the scale.

Active Publication Date: 2022-04-22
CENT SOUTH UNIV
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Problems solved by technology

[0005] The invention provides a compressor fault classification method based on the integrated pruning strategy of a balanced binary tree. poor performance

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  • Compressor Fault Classification Method Based on Balanced Binary Tree Integrated Pruning Strategy
  • Compressor Fault Classification Method Based on Balanced Binary Tree Integrated Pruning Strategy
  • Compressor Fault Classification Method Based on Balanced Binary Tree Integrated Pruning Strategy

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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 the existing problems, the present invention provides a compressor fault classification method based on a balanced binary tree integrated pruning strategy, 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-...

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Abstract

The present invention provides an integrated pruning strategy based on a balanced binary tree. The method includes the following steps: S1, initializing the integrated pool of the base classifier: segmenting a large data set to form many sub-data sets, and then targeting each sub-data The set is used for training and testing to form an initial complete classifier pool; S2. Construct a balanced binary tree to form the final sub-integration; S3. Use the best retained sub-integration to predict and classify new data samples. The invention solves the technical problems that fitting phenomenon is easy to occur, it is difficult to eliminate base classifiers with too high or too low test accuracy, and the generalization performance is not high.

Description

technical field [0001] The invention relates to the technical field of integrated learning, in particular to a compressor fault classification method based on a balanced binary tree integrated pruning strategy. 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 algorit...

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Application Information

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Patent Type & Authority Patents(China)
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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