Evolutionary ensemble learning method for classification problems

A learning method, a technology of questions, applied in the field of ensemble learner search group

Pending Publication Date: 2021-04-30
XIAN UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] Aiming at the problems existing in the existing ensemble learning, this paper invents an automatic ensemble learning method with dual evolution architecture for classification problems, by establishing the evolutionary search of the bas

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  • Evolutionary ensemble learning method for classification problems
  • Evolutionary ensemble learning method for classification problems
  • Evolutionary ensemble learning method for classification problems

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Embodiment Construction

[0050] The present invention will be further described below in conjunction with the accompanying drawings. In the following, the Bupa problem is taken as an example to illustrate the technical solution of the present invention and its implementation process, which cannot be used to limit the protection scope of the present invention. Such as figure 1 Shown is the process of a dual-evolutionary architecture automatic ensemble learning method for classification problems, and its specific implementation steps are as follows:

[0051] Step 1: Initialize the individual base learner population, and set the base learner population size to 20. The features extracted from the base learner for tree encoding in the Bupa dataset and the segmentation thresholds as figure 2 shown;

[0052] Step 2: The search process of the base learner individual. For example, exchange part of the branches of No. 1 tree and No. 2 tree to obtain No. 3 and No. 4 subtrees, such as image 3 As shown; the...

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Abstract

The invention discloses an evolutionary ensemble learning method for classification problems, and belongs to the field of machine learning and data mining. The method comprises the following steps: firstly, generating a group of base learner groups based on tree coding and a corresponding integrator group based on binary coding for a classification problem; then, performing multi-objective evolutionary search of a base learner group and combined optimization search of an ensemble learner group; two levels of evolutionary search processes are combined and mutually fed back in information, so that a high-quality base learner group is constructed, and a high-accuracy calculation target of integrated learner individuals is generated. According to the method, two levels of search optimization processes can be dynamically combined through an evolutionary iteration mechanism, and then collaborative optimization is realized through information exchange between the search optimization processes, so that a base learner group is promoted to evolve continuously, and balance between high quality of individuals and diversity of the group is kept; Meanwhile, global optimization of the integrated learner structure is realized.

Description

technical field [0001] The invention belongs to the field of machine learning and data mining, and specifically includes a base learner search group based on tree coding and an integrated learner search group based on binary code. The two levels of search optimization processes can be dynamically combined through the evolutionary iterative mechanism, and then the collaborative optimization can be realized through the information exchange between each other. This architecture can promote the continuous evolution of the base learner population and maintain a balance between the high quality of individuals and the diversity of the population, while achieving global optimization of the ensemble learner structure. In addition, this computing architecture is beneficial to reduce the dependence of the construction process of ensemble learners on human design decisions, and instead realize the automatic generation of ensemble learners through data-driven models. Background technique...

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Application Information

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IPC IPC(8): G06N20/20G06N3/12G06N3/00
CPCG06N3/006G06N3/126G06N20/20
Inventor 陈皓张国鑫贾蓉
Owner XIAN UNIV OF POSTS & TELECOMM
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