Feature selection method and device, equipment and storage medium

Abstract

The invention discloses a feature selection method and device, equipment and a storage medium. The method comprises the steps that any execution node receives an initial selected feature set broadcasted by a management node and feature data corresponding to the initial selected feature set; constructing at least one first candidate feature set; obtaining a model performance index corresponding to the at least one first candidate feature set; in response to the first candidate feature set meeting the selection condition in the at least one first candidate feature set, the first candidate feature set meeting the selection condition is sent to a management node, and the management node is used for obtaining a target selected feature set based on the first candidate feature set meeting the selection condition. In the above process, feature selection can be directly realized based on the stored complete feature data, the selected feature set broadcasted by the management node and the feature data corresponding to the selected feature set, feature data interaction between execution nodes is not needed, the time consumption of the feature selection process is relatively short, the feature selection efficiency is relatively high, and the implementation efficiency is relatively high. And the feature selection effect is relatively good.

Description

technical field

[0001] The embodiments of the present application relate to the computer field, and in particular to a feature selection method, device, equipment, and storage medium. Background technique

[0002] With the continuous improvement and development of computer storage and computing capabilities, in the field of machine learning, many high-dimensional data sets are often involved. The original feature set corresponding to a high-dimensional data set usually contains a large number of redundant features, and these redundant features will reduce the processing performance of the machine learning model. Feature selection can select the features that work for the machine learning model from the original feature set, and then only use the data set corresponding to the selected features to perform the training or use process of the machine learning model, thereby reducing the computational complexity of the machine learning model and improving the performance of the ma...

Examples

Embodiment Construction

[0051] In order to make the purpose, technical solution and advantages of the present application clearer, the implementation manners of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0052] In order to facilitate the understanding of the technical process of the embodiment of the present application, some nouns involved in the embodiment of the present application are explained below:

[0053] Machine learning: Machine learning is the science of making computers learn and act like humans. It uses models to learn the hidden knowledge under a large amount of data, and uses optimization algorithms to optimize the models. At present, it has been widely used in various fields, such as shopping recommendation, search ranking, advertisement click, credit risk assessment, image recognition, automatic driving and other fields.

[0054] Feature Engineering: Feature engineering refers to the process of using domain know...

technical field

[0001] The embodiments of the present application relate to the computer field, and in particular to a feature selection method, device, equipment, and storage medium. Background technique

[0002] With the continuous improvement and development of computer storage and computing capabilities, in the field of machine learning, many high-dimensional data sets are often involved. The original feature set corresponding to a high-dimensional data set usually contains a large number of redundant features, and these redundant features will reduce the processing performance of the machine learning model. Feature selection can select the features that work for the machine learning model from the original feature set, and then only use the data set corresponding to the selected features to perform the training or use process of the machine learning model, thereby reducing the computational complexity of the machine learning model and improving the performance of the ma...

Examples

Embodiment Construction

[0051] In order to make the purpose, technical solution and advantages of the present application clearer, the implementation manners of the present application will be further described in detail below in conjunction with the accompanying drawings.

[0052] In order to facilitate the understanding of the technical process of the embodiment of the present application, some nouns involved in the embodiment of the present application are explained below:

[0053] Machine learning: Machine learning is the science of making computers learn and act like humans. It uses models to learn the hidden knowledge under a large amount of data, and uses optimization algorithms to optimize the models. At present, it has been widely used in various fields, such as shopping recommendation, search ranking, advertisement click, credit risk assessment, image recognition, automatic driving and other fields.

[0054] Feature Engineering: Feature engineering refers to the process of using domain know...

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