Data feature processing method and data feature processing apparatus

A data feature processing apparatus, applied to a hardware structure of a data processing server, including a processor and a memory, further comprising:

• a data acquisition module, configured to obtain a plurality of groups of business data, wherein the groups of business data comprises user data obtained using application software, further wherein each group of business data comprises n data features, and n is a positive integer;
• a model establishment module, configured to:
• sort the plurality of groups of business data in a reverse chronological order of business data obtaining time to obtain a business data sorting sequence;
• determine highly-ranked business data of a first predetermined proportion as a cross-time validation set, and lowly-ranked business data of a second predetermined proportion as modeling sample data;
• and take a data feature corresponding to the modeling sample data as a model feature, and establish a recognition model by using a preset classifier, wherein a sum of the first predetermined proportion and the second predetermined proportion is 1;
• a matrix calculation module, configured to:
• calculate feature importance values of the n data features based on the recognition model and a gain indicator of the recognition model, and calculate a correlation matrix of each data feature in the n data features by taking the modeling sample data as a benchmark, wherein the correlation matrix is an n*n matrix;
• a set determining module, configured to determine a plurality of target sets in the correlation matrix, wherein there is no duplicate element between different target sets;
• a feature deletion module, configured to:
• delete a data feature with a maximum feature importance value in each target set, merge remaining data features in each target set to obtain a feature set, and delete a data feature in the n data features being identical to a data feature in the feature set to obtain to-be-selected model features, wherein the feature set comprises m data features, a number of the to-be-selected model features is n−m, and m is a positive integer less than n;
• a data calculation module, configured to:
• import the to-be-selected model features into the preset classifier in batches in a descending order of the feature importance values, and obtain a difference between two pieces of adjacent first performance indicator data that is calculated by the preset classifier;
• determine whether the difference meets a preset condition;
• when the difference meets the preset condition, determine an automation model feature in the to-be-selected model features, and calculate second performance indicator data of the recognition model in the cross-time validation set;
• and determine model benchmark performance data based on the difference and the second performance indicator data;
• and
• an associative-storage module, configured to associatively store the automation model feature and the model benchmark performance data;
• wherein deleting the data feature reduces operation time and memory demands in a model establishment process, reduce model complexity to facilitate practical application and maintenance of the model, and reduce resources consumed for model operations while ensuring the model performance.