Method and device for determining computer-executed integrated model

Abstract

The embodiment of the invention provides a method for determining a computer-executed integrated model, and the method comprises the steps: firstly, obtaining a current integrated model and a plurality of untrained candidate sub-models; secondly, respectively integrating each sub-model in a plurality of candidate sub-models into the current integrated model to obtain a plurality of first candidateintegrated models; then, at least training the plurality of first candidate integration models to obtain a plurality of second candidate integration models after the current training; then, carryingout performance evaluation on each second candidate integration model in the plurality of second candidate integration models to obtain a corresponding performance evaluation result; then, on the basis of the performance evaluation result, determining an optimal candidate integration model with optimal performance from the plurality of second candidate integration models; furthermore, under the condition that the performance of the optimal candidate integration model meets a preset condition, updating the current integration model by utilizing the optimal candidate integration model.

Description

technical field [0001] One or more embodiments of this specification relate to the field of machine learning, and in particular, to a method and device for automatically determining an integrated model using a computer. Background technique [0002] Integrated learning is a machine learning method that uses a series of individual learners, or sub-models, to learn, and then integrates the learning results to obtain better learning results than a single learner. Usually in integrated learning, a "weak learner" is first selected, and then multiple learners are generated by sample set disturbance, input feature disturbance, output representation disturbance, algorithm parameter disturbance, etc., and then integrated to obtain a better accuracy The "strong learner", or ensemble model. [0003] However, at present, integrated learning relies heavily on expert experience and manual debugging. Therefore, there is an urgent need for an improved solution that can reduce the dependen...

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Problems solved by technology

[0003] However, at present, integrated learning relies heavily on expert experience and manual debugging.

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