A Bayesian weighting method based on cfs_kl

Abstract

The invention discloses a novel Bayesian weighting method based on CFS_KL, which uses the fingerprint name in the fingerprint library as the marking data of the model, and the response sequence flag bits under the fingerprint name constitute the training data; the training data is sealed and preprocessed ;Use the KL divergence to calculate the degree of association between the attribute and the class as the weight of each attribute; use the feature selection method to select 42 dimensions; use the dimension selected by CFS to modify the weight calculated by the KL divergence; use the weighted Bay The Yesian algorithm is used for training; the vector is input into the trained fingerprint model through the sealing operation, and the maximum posterior probability of each flow is calculated based on the CFS_KL weighted Bayesian algorithm, and the simulated data test is completed; by sending packets to the target network segment , collect the real traffic, input the real traffic into the fingerprint model, and predict the result; calculate the test accuracy of the real traffic. The invention alleviates the requirement of the Bayesian algorithm on feature independence, and improves the recognition accuracy of the Bayesian algorithm.

Description

technical field [0001] The invention belongs to the technical field of machine learning, in particular to a Bayesian weighting method based on CFS_KL (correlation-based feature selection_Kullback-Leibler). Background technique [0002] As one of the ten classic algorithms of machine learning, Bayesian algorithm has many applications in many fields, and all of them have shown very good results. For example, judging whether an email is spam or not based on its title and content. However, since learning the optimal Bayesian classifier is just like learning the Bayesian network, it is an NP-hard problem, so learning the Naive Bayesian classifier has been favored by many scholars, and Naive Bayesian is often based on a simple But unrealistic assumption: The features of the training data are independent of each other. This strong condition is difficult to achieve in real life. Even in reality, it has logically shown that the features are independent of each other. In the actual da...

AI Technical Summary

Problems solved by technology

However, since learning the optimal Bayesian classifier is just like learning the Bayesian network, it is an NP-hard problem, so learning the Naive Bayesian classifier has been favored by many scholars, and Naive Bayesian is often based on a simple But unrealistic assumption: the features of the training data are independent of each other. This strong condition is difficult to achieve in real life. Even in reality, it has logically shown that the features are independent of each other. In the actual data Not so independent, which greatly limits the classification effect of Naive Bayes

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