Estado del arte de algoritmos de Machine Learning para la detección de rupturas súbitas.

In this work, a review of the existing paradigms and the most used techniques in the burst detection is carried out, delving into those that use Machine Learning as the main tool for data interpretation. The relationship between detection effectiveness and the parameters of each algorithm, as well as the level of processing required, are compared. For Support Vector Machine, the effectiveness in burst detection is exponentially related to the number of combinations of C and γ. The exposed decision tree increases its precision the more information about the state of the network it has. The artificial neural network demonstrates a detection effectiveness at the level of the rest of the algorithms treated, maintaining the commitment to the processing level. [ABSTRACT FROM AUTHOR]