Feature extraction method and machine learning method and device thereof

Abstract

The invention provides a feature extraction method and a machine learning method and a device thereof. The feature extraction method comprises the steps that data records are acquired; feature extraction configuration items used for limiting how to extract preset features from the data records are acquired, wherein the feature extraction configuration items of each preset feature comprise the source field item and the processing method item, the source field items are used for limiting fields of the data records relevant to each preset feature into source fields, the processing method items are used for assigning quotation on data processing functions which are pre-programmed into executable codes, and the data processing functions are used for executing data processing for extracting all the preset features for field values of the source fields limited by the source field items; data processing is performed on the field values of the data records on the basis of the feature extraction configuration items to acquire feature values of the preset features. According to the feature extraction and machine learning technique, the programming flexibility and the code reusability are enhanced, and the technique is particularly suitable for big data application.

Description

technical field [0001] The present invention generally relates to the field of information technology, and more specifically relates to a feature extraction method, a machine learning method and a corresponding device. Background technique [0002] In the field of information technology such as data mining and machine learning, the object of processing is data. Before processing the vast data, it is usually necessary to extract the features of the data. [0003] Features can be used as raw materials for data processing. Simply put, each data record can include multiple fields, and features can indicate each field itself, or a part of a field, or a combination of fields, or a field transformation or other processing results, etc. To help better reflect the internal correlation and potential meaning of the data distribution. Taking the field of data mining as an example, features are the raw materials of machine learning systems and have a significant impact on the final mode...

AI Technical Summary

Problems solved by technology

This not only brings a heavy workload to the programmer, but also consumes a large amount of overhead in program operation.

In view of the diversity of actual application scenarios and data specifications, this brute force approach is difficult to expand and reuse

[0015] Therefore, the existing idea of ​​developing a set of different processing procedures for each data format and extracted content is to traverse the scale of the problem, resulting in a non-linear increase in the development complexity of feature extraction, and at the same time, it is difficult to constrain the operational complexity.

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