Feature processing method and device for user classification model

Abstract

The embodiment of the invention provides a feature processing method and device for a user classification model. The method comprises the steps that firstly, label data tables and first feature tablesare acquired, wherein each first feature table records a plurality of features of a user; and for each feature in each first feature table, a feature IV value is calculated, and first screening operation is performed on the features based on the IV value to obtain a corresponding second feature table. Then, the second feature tables and features in the second feature tables serve as first class nodes and second class nodes respectively, a bipartite graph is constructed, the minimum number of the first class nodes connected to all the second class nodes is determined in the bipartite graph, and then corresponding M second feature tables are obtained; then, the M second feature tables are combined to obtain a comprehensive feature table, and correlation coefficients between features are calculated based on the comprehensive feature table; and a second screening operation is performed on the features based on the correlation coefficient to obtain a plurality of selected features for training a user classification model.

Description

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AI Technical Summary

Benefits of technology

The technical effects described are that these systems use different techniques such as binary or ternary scoring to quickly identify users' preferences from their data set. This allows them to select representative attributes without having to manually input any details about each attribute into an analysis toolbox before making changes. Additionally, they allow for customization with specific types of interactions (such as social media) while still maintaining accurate results. Overall, these improvements make learning new things faster than traditional methods like manual annotation.

Problems solved by technology

This patents discusses improving the efficiency at selecting and analyzing featurities during computer networked simulations or other applications where high computing resources must be allocated among numerous small databases containing diverse types of data. Current methods require significant time and effort by experts working alongside each database individually beforehand, making them difficult to automate effectively.

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