Emergent event classification and grading method, device and system based on decision trees and Bayesian algorithm

Abstract

The invention relates to an emergent event classification and grading method, device and system based on decision trees and the Bayesian algorithm. The method comprises steps of S1, carrying out characteristic division on a preset grading and classification event library and constructing a training sample set; S2, according to the training sample set, using the ID3 algorithm, the C4.5 algorithm, and the CART algorithm to construct three decision tree classification and grading models; S3, according to the training sample set, constructing and training a Bayesian classifier; S4, carrying out key characteristic attribute extraction on events which are to be classified and graded; S5, according to event characteristic attributes, using the three decision tree models to carry out classification so as to obtain three classification results; and S6, according to the event characteristic attributes, using the Bayesian classifier to calculate probability of the categories of the three classification results in the S5, and taking the highest probability to be the final classification result. According to the invention, classification accuracy of a single algorithm can be improved; and a disadvantage of difficulty in predicting continuous fields in a decision tree algorithm is effectively overcome.

Description

technical field [0001] The invention relates to the technical field of smart cities, in particular to a method, device and system for classifying and grading emergencies combining a decision tree algorithm and a Bayesian algorithm. Background technique [0002] In the field of public security command and control, the matching of contingency plans and historical plans is a key step to improve the efficiency of incident handling. The matching of contingency plans and historical plans depends on the classification and grading of events. At present, there are usually two forms of grading and classification of emergencies at home and abroad: one is purely manual judgment. According to the situation of historical emergencies, the relevant core features are manually summarized to form an index system. Indicators, manually judge the type and level of the event; the second is manual + automatic judgment, first summarize the core characteristics of the emergency manually, and form an...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects existing in the prior art, the present invention provides a method, device and system for classification and grading of emergencies based on the decision tree algorithm and the Bayesian algorithm, which effectively compensates for the difficult prediction of the continuous fields by the decision tree algorithm, When there are too many categories, errors may increase and perform poorly when dealing with data with strong feature correlations.

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