Development of an intelligent system for evaluating the effectiveness of law enforcement activities of the territorial bodies of the Ministry of Internal Affairs of Russia based on multicriteria analysis and machine learning methods
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Abstract
The article presents the results of the development and testing of an intelligent system for evaluating the effectiveness of law enforcement activities of the territorial bodies of the Ministry of Internal Affairs of Russia, based on the integration of multicriteria analysis and machine learning methods. The relevance of the research is determined by the need to improve the tools for an objective assessment of the activities of law enforcement agencies in the context of the digital transformation of public administration. The methodological basis of the developed system is a synthesis of ontological engineering, dynamic modification of the TOPSIS method and an ensemble of predictive machine learning models. Empirical validation was carried out on an array of data from five regional departments of the Ministry of Internal Affairs of Russia for the period 2018-2023, including 57 performance indicators and 23 environmental factors. The results demonstrate a significant increase in the accuracy of estimates (by 17.6% compared to traditional methods), a decrease in the variance of estimates (the coefficient of variation decreased from 0.31 to 0.18) and an improvement in predictive ability (the average error of the MAPE forecast was 8.3%). The developed intelligent system provides context-dependent parameterization of criteria, adaptive ranking of departments taking into account regional specifics and proactive identification of problem areas. The proposed approach creates a methodological basis for the transition from static retrospective assessment methods to dynamic multifactorial analysis of the effectiveness of law enforcement activities.
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References
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