Feature combination method, apparatus, medium and electronic device

Abstract

The present application provides a feature combination method, device, medium, and electronic equipment, the method including: using multiple samples including multiple different feature domains as the input of the neural network model, and obtaining the corresponding explanation of each feature domain in the multiple samples Vector; mark the feature domains whose interpretation vectors meet the preset conditions, wherein the preset conditions include that the absolute value of the interpretation vector is greater than the set threshold; according to the order of the frequency of each feature domain being marked from more to less, sequentially select A preset number of combined feature domains are used as candidate combined feature domains; a target combined feature domain matching the target model is selected from multiple candidate combined feature domains. This application obtains the respective interpretation vectors corresponding to multiple different feature domains, thereby screening candidate combined feature domains that have a better impact on the output of the neural network model, narrowing the selection range of combined feature domains, and reducing the complexity of screening combined feature domains degree, and the resulting combined feature domain is interpretable.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a feature combination method, device, medium and electronic equipment. Background technique [0002] Automatic machine learning technology is gradually attracting attention in various fields because it can realize automatic data processing and modeling without deep mining of data. When using automatic machine learning technology for modeling, the selection of combined features has a significant impact on the accuracy of the neural network model. Combination features are formed by the intersection of multiple different feature domains. [0003] At present, when selecting combined features, one of the mutual information complete search strategy, brute force search strategy, and deep model dot product strategy is mostly used to realize feature domain interaction, so as to search out the feature combination required for the neural network model to predict the prediction ob...

AI Technical Summary

Problems solved by technology

However, the mutual information complete search strategy only uses the mutual information value as the screening condition of the combined features, which not only lacks generalization, but is only suitable for some classification tasks, and makes it difficult for the combined features to improve the prediction effect of the model.

However, the brute force search strategy needs to traverse too many combination features, which not only has a high failure rate, but also leads to a large time cost.

The combined features selected by the depth model dot product strategy are highly complex, difficult to be understood by humans, and not interpretable

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