  Gamma-ray bursts (GRBs), as extremely energetic explosions in the universe,
are widely believed to consist of two stages: the prompt phase and the
subsequent afterglow. Recent studies indicate that some high-energy photons are
emitted earlier at source than the prompt phase. Due to the light speed
variation, these high-energy photons travel slowly than the low-energy photons,
so that they are observed after the prompt low-energy photons at the detector.
Based on the data from the Fermi Gamma-ray Space Telescope (FGST), we analyse
the photon distribution before the prompt emission in detail and propose the
existence of a hitherto unknown pre-burst stage of GRBs by adopting a
classification method of machine learning. Analysis on the photons
automatically selected by machine learning also produce a light speed variation
at $E_{\mathrm{LV}}=\mathrm{3.55\times 10^{17} GeV}$.
