Classification and Prioritization of Tasks in Public Administration

Abstract

A large volume of data is being generated in public administration and it is necessary to develop new computational methodologies to classify and analyze it to do a better analysis and decision making. For this reason, the goal of this paper is to present a computational methodology that allows classifying and prioritizing a series of complaints using Artificial Intelligence techniques. To test this model, we generate 600 complaints in four sectors of the public administration to prove the concept. Later, we calculated the tree decision with the help of the Confusion Matrix, and finally the Priority Matrix (based on the Eisenhower model) allows setting priorities on the complaints, and offers the possibility of delegating and even postponing the response to them. In this way, it is possible to prioritize the complaints made in the public administration.