Alarm handling and receiving information scoring method based on Bayes prediction

Abstract

The invention discloses an alarm handling and receiving information scoring method based on Bayes prediction. Firstly, personal information is matched through a regular expression for word segmentation; then, a text type is predicted according to a word segmentation result, and the category which a text belongs to is judged through a naive Bayes algorithm according to the word segmentation resultand internal classification feature samples to obtain the probability that one text belongs to a case category; finally, data collision is performed through a data collision model graph to complete warning condition correlation. The category probability of one warning condition text classification is obtained by adopting a Bayes classifier according to the warning condition word segmentation result and a warning condition keyword library, then the category probability and warning condition weight are accumulated through a decision-making tree to obtain a scoring result, and collision completedbased on importance distinguishing of warning conditions after analysis of all feature information of the warning conditions can be achieved in the mode that relevant texts are associated by extracting special identifiers of warning condition texts, for example, identity numbers.

Description

technical field [0001] The invention belongs to the technical field of text mining, and in particular relates to a Bayesian classification of police text data and a decision tree scoring algorithm for cases. Background technique [0002] Text data mining is divided into text classification and text prediction. Text classification refers to extracting text features and labels through classification and regression; text prediction is to obtain text features and partial labels through classification, regression, and clustering. Existing text classification techniques generally perform word segmentation first, and then use supervised learning algorithms to directly classify the text. [0003] For the processing of police cases, the current common method is manual classification, which is time-consuming and error-prone, and may easily cause errors in judging key police cases. Supervised learning classification and scoring can avoid human errors as much as possible. [0004] How...

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Problems solved by technology

[0003] For the processing of police situations, the current common method is manual classification, which is time-consuming and error-prone, and is likely to cause errors in judging key police situations. Supervised learning classification and scoring can avoid human errors as much as possible

[0004] However, the disadvantage of the above-mentioned text classification technology is that it cannot judge the importance of the text after each classification, ignores the calculation of the loss rate of the text after the probability analysis, and does not perform in-depth processing on the collision of the feature extraction data of the text.

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