  For transient sources with timescales of 1-100 seconds, standardized imaging
for all observations at each time step become impossible as large modern
interferometers produce significantly large data volumes in this observation
time frame. Here we propose a method based on machine learning and using
interferometric closure products as input features to detect transient source
candidates directly from the spatial frequency domain without imaging. We train
a simple neural network classifier on a synthetic dataset of
Noise/Transient/RFI events, which we construct to tackle the lack of labelled
data. We also use the hyper-parameter dropout rate of the model to allow the
model to approximate Bayesian inference, and select the optimal dropout rate to
match the posterior prediction to the actual underlying probability
distribution of the detected events. The overall F1-score of the classifier on
the simulated dataset is greater than 85\%, with the signal-to-noise at
7$\sigma$. The performance of the trained neural network with Monte Carlo
dropout is evaluated on semi-real data, which includes a simulated transient
source and real noise. This classifier accurately identifies the presence of
transient signals in the detectable signal-to-noise levels (above 4$\sigma$)
with the optimal variance. Our findings suggest that a feasible radio transient
classifier can be built up with only simulated data for applying to the
prediction of real observation, even in the absence of annotated real samples
for the purpose of training.
