Data processing method and device based on artificial intelligence and computer equipment

Abstract

The invention relates to a data processing method and device based on artificial intelligence, computer equipment and a storage medium. The method comprises the steps of obtaining a preset number of samples, wherein the samples comprise user features and user behavior sequences; performing sequence pattern mining based on the user behavior sequence to obtain a plurality of user behavior sequence patterns; identifying the relevancy between the user characteristics and the user behavior sequence mode, and extracting a strong correlation sequence mode corresponding to the user characteristics according to the relevancy; counting characteristic value distribution corresponding to each user characteristic in the strong correlation sequence mode; and when one sample has the user feature with themissing feature value, filling the missing feature value according to the feature value distribution to obtain a sample filled with the feature value. A sample containing accurate and effective userfeatures can be provided for model learning, and then the accuracy of machine learning is improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based data processing method, device, computer equipment and storage medium. Background technique [0002] With the development of artificial intelligence technology, technologies such as big data analysis and machine learning have emerged. For big data analysis and machine learning, the quality of data samples often plays a crucial role. The sample features in the data samples can determine the model effect and learning ability. In practical applications, missing eigenvalues ​​of sample features occur frequently, which will cause deviations in modeling learning and model prediction. [0003] For the processing of missing values, the traditional method usually uses missing value filtering or missing value filling. The missing value filtering method is to directly delete the features containing missing values, which will lead...

AI Technical Summary

Problems solved by technology

The missing value filtering method is to directly delete the features containing missing values, which will lead to information loss. When the feature dimension is not large enough, it will affect the reference information for model learning.

The way to fill missing values ​​is to use the mean value of continuous features to fill, or to use the mode of discrete features to fill. Since the impact of the filled feature value on the category label prediction is not considered, it is easy to introduce noise and affect model learning.

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