Dangerous tendency prediction method based on prisoner behavior characteristic ensemble learning model

Abstract

The invention relates to the big-data processing technology in computer intelligent information processing, in particular to a dangerous tendency prediction method based on a prisoner behavior characteristic ensemble learning model. The dangerous tendency prediction method comprises the following steps: collecting the monitoring data of a prisoner, preprocessing the data, and carrying out corresponding category marking to the data which does not exhibit extreme behavior tendency and exhibits the specific extreme behavior tendency; taking the marked data as a training set, training on the training set by utilizing a dispatching ensemble learning algorithm to obtain an ensemble learning model consisting of a plurality of classification models; and predicting and classifying the data which does not mark the category by utilizing the ensemble learning model. The invention has the beneficial effects that the prediction method based on the dispatching ensemble learning algorithm generates a plurality of different classification models and combines the classification results of the classification models, so that a defect of poor stability generated when only one classification model is used can be overcome, and the invention has the advantages of being high in classification precision, classification stability and early warning accuracy.

Description

technical field [0001] The invention relates to a big data processing technology in computer intelligent information processing, in particular to a dangerous tendency prediction method based on an integrated learning model of behavioral characteristics of inmates. Background technique [0002] With the development of prison informatization, a large number of criminal management business systems including prison administration system, criminal law enforcement system, life sanitation system, family telephone system, interview management system, psychological counseling system, etc. have been built, as well as video surveillance system, Access control system, alarm system, digital power grid, external vehicle and personnel entry and exit management system, emergency command auxiliary decision-making system and a large number of security systems. The construction of these systems provides a large amount of effective basic data for the collection, analysis, and judgment of prison...

AI Technical Summary

Problems solved by technology

[0004] For the existing classification methods, there are also the following deficiencies: (1) When classifying the prisoners, they did not take into account the various types of extreme behavior tendencies that the prisoners may have, such as escape, suicide, violence, destruction, etc.

(2) The existing methods for the classification of inmates rely heavily on artificially designed indicators, the degree of intelligence of the method is not high, and the large amount of data generated by the existing business system is not fully utilized to find abnormal behavior characteristics of inmates

If the prediction performance of the classification model is poor, it will affect the accuracy and stability of the classification in practical applications, so that the ideal classification accuracy and stability cannot be guaranteed.

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