  Gamma-Ray bursts (GRBs) have been traditionally classified based on their
duration. The increasing number of extended emission (EE) GRBs, lasting
typically more than 2 seconds but with properties similar to those of a short
GRBs, challenges the traditional classification criteria. In this work, we use
the t-Distributed Stochastic Neighbor Embedding (t-SNE), a machine learning
technique, to classify GRBs. We present the results for GRBs observed until
July 2022 by the Swift/BAT instrument in all its energy bands. We show the
effects of varying the learning rate and perplexity parameters as well as the
benefit of pre-processing the data by a non-parametric noise reduction
technique FABADA. Consistently with previous works, we show that the t-SNE
method separates GRBs in two subgroups. We also show that EE GRBs reported by
various authors under different criteria tend to cluster in a few regions of
our t-SNE maps, and identify seven new EE GRB candidates by using the gamma-ray
data provided by the automatic pipeline of Swift/BAT and the proximity with
previously identified EE GRBs.
