Data processing method and device based on principal component analysis and storage medium

Abstract

The embodiment of the invention relates to the field of software defect prediction, and discloses a data processing method and device based on principal component analysis, and a computer readable storage medium, and the method comprises the steps: carrying out the dimension reduction of initial sample data, and obtaining the sample data of a preset dimension; acquiring a plurality of features ofthe sample data, and calculating the relevancy between each feature and a preset category, the preset category being one of a plurality of categories of the sample data; and removing the features of which the relevancy is less than the preset relevancy in the plurality of features, and taking the remaining features as identification features of the sample data. According to the data processing method and device based on principal component analysis and the computer readable storage medium provided by the invention, redundant features in the sample data can be removed, and the sample data withhigh discrimination is obtained, so that the prediction efficiency is improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of data processing, and in particular to a principal component analysis-based data processing method, device, and computer-readable storage medium. Background technique [0002] Information entropy is a measure of the amount of information required to eliminate uncertainty, that is, the amount of information that an unknown event may contain. An event or a system, to be precise, is a random variable with certain uncertainties. The uncertainty of some random variables is very high. To eliminate this uncertainty, a lot of information needs to be introduced, and the measurement of this much information is expressed by "information entropy". The more information that needs to be introduced to eliminate uncertainty, the higher the information entropy, and vice versa. If a situation has high certainty, little information needs to be introduced, so the information entropy is very low. According t...

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Problems solved by technology

[0003] The inventors found that there are at least the following problems in the prior art: analyzing the characteristics of the sample data according to the above formula, many redundant features are obtained, resulting in low prediction efficiency of the model trained by using the sample data

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