Badcase discovery method and system based on small sample learning

A discovery method and small sample technology, applied in the fields of instruments, character and pattern recognition, computer components, etc., can solve the problems of biased data sources, inability to find bad cases, and small random coverage, and achieve the effect of reducing time-consuming

Inactive Publication Date: 2020-04-21
CHENGDU XIAODUO TECH CO LTD
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  • Abstract
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AI Technical Summary

Problems solved by technology

Using random sampling technology (random sample), randomly thousands of online prediction results, and then hand them over to humans for labeling and judgment. The manpower is limited, the random coverage is small, and it cannot cover as many badcases as possible.
Use the threshold se

Method used

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  • Badcase discovery method and system based on small sample learning
  • Badcase discovery method and system based on small sample learning
  • Badcase discovery method and system based on small sample learning

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Embodiment Construction

[0026] The present invention will be further described below in conjunction with accompanying drawing:

[0027] Such as figure 1 As shown, a badcase discovery method based on small sample learning includes the following steps:

[0028] S1: Data preprocessing, randomly obtain multiple small samples from the marked training corpus, and divide the samples into support set and target set, the small samples are in the data form of N-way K-shot, N represents each The number of semantics included in the small training batch, K represents the number of training samples under each semantic, N is generally less than 100, and K is generally less than 20. Each small training process data is divided into a support set and a target set. Generally, the same N-way K-shot form is used. After the model is trained once on the support set, the loss of the model will be obtained under the paired target set (loss function value) for backpropagation to update model parameters. The labeled trainin...

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Abstract

The invention discloses a badcase discovery method and system based on small sample learning, and the system applies the method, and the method comprises the steps: carrying out the data preprocessing, randomly obtaining a plurality of small samples from a labeled training corpus, and dividing the samples into a support set and a target set; pre-training the model, inputting the sample into a basic model, combining the basic model with two attention mechanisms, and performing training to obtain a badcase discovery model; predicting data assembly: according to online prediction result semantics, obtaining small samples under the online prediction result semantics from the labeled training corpus, and forming prediction data by the small samples and the sentence to be predicted; and performing data prediction: inputting the prediction data into the badcase discovery model, performing prediction to obtain prediction semantics of a to-be-predicted sentence, comparing the prediction semantics with online prediction result semantics, and judging whether the to-be-predicted sentence is badcase or not. By adopting the method to discover the badcase, quick positioning can be performed in massive data, and error data can be accurately obtained.

Description

technical field [0001] The invention belongs to the technical field of computer data processing, and in particular relates to a badcase discovery method and system based on small sample learning. Background technique [0002] In supervised classification learning, a model with a good offline test set effect often has a certain gap when it is applied online. This requires continuous optimization to address these gaps, so that the online application effect of the model is getting better and better. The performance of these gaps is that when the model predicts and applies online data, the proportion of misclassified data (often referred to as badcase) will be high. The optimization process needs to find these misclassified data, and then analyze the wrong ones in a targeted manner. cause and resolve. The amount of online data is very large, often tens of millions or even billions of visits. If you rely on manpower, it will be very time-consuming and labor-intensive to locate b...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/217G06F18/214
Inventor 郭涛江岭
Owner CHENGDU XIAODUO TECH CO LTD
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