Method and system for data classification based on machine learning

Abstract

The present invention relates to a method for data classification based on machine learning. The method comprises the steps of: forming a first batch of a plurality of classification models corresponding to a first batch of a plurality of machine learning algorithms; employing each classification model in the first batch of the classification models to perform classification calculation of first data features; establishing an Nth batch of a plurality of iteration models, and performing learning training based on second data features; employing each iteration model to perform classification calculation of the second data features; measuring an approaching degree of a second classification result and an expected classification result; if the approaching degree meets a first condition, finishing the method; or else, perform iteration execution of the establishing step of the iteration models. The method can achieve an effect of similar deep learning so as to greatly improve the universality and the accuracy of data classification. The data classification method is high in reliability and low in artificial intervention degree.

Description

technical field [0001] The present invention relates to the technical field of machine learning, and more specifically, relates to a data classification method and system based on machine learning. Background technique [0002] In the field of big data security analysis, when identifying normal / dangerous labels, such as the identification and classification of malicious web pages, it is often necessary to first evaluate and analyze scenarios and problems based on security personnel, and then select specific machine learning algorithms for training and testing , but the recognition results will depend too much on the suitability of a single machine learning algorithm and the time of model tuning, and usually cannot have a certain universality, that is, the ability of knowledge transfer. [0003] Before machine learning algorithms identify and classify things, they must conduct precise analysis and research on business scenarios, that is, technicians with professional knowledg...

AI Technical Summary

Problems solved by technology

This method can intelligently detect security threats, but it has high requirements for algorithm selection and model optimization. If no suitable algorithm is selected, the final classification effect may not be ideal

[0007] 2. Model parameters are extremely sensitive to the classification effect

[0009] 3. An algorithm has a significant impact on the combined effect of multiple classifiers

However, if one part of the algorithm performs poorly in recognition, it will affect the summary results; moreover, if different parts of the algorithm produce conflicting results, the classifier will be in a dilemma, and human intervention is again required

[0011] 4. The main and auxiliary combination method is too closely related to the scene

Even though the advantage of this method lies in the combined application of multiple algorithms, the application of "combiner" or "voter" is easy to introduce a considerable degree of subjective or empirical factors into the classification process, making the classification process deviate from the machine Learning - the goal of machine classification

Images