  The Coherent WaveBurst (cWB) search algorithm identifies generic
gravitational wave (GW) signals in the LIGO-Virgo strain data. We propose a
machine learning (ML) method to optimize the pipeline sensitivity to the
special class of GW signals known as binary black hole (BBH) mergers. Here, we
test the ML-enhanced cWB search on strain data from the first and second
observing runs of Advanced LIGO and successfully recover all BBH events
previously reported by cWB, with higher significance. For simulated events
found with a false alarm rate less than $1\,\mathrm{yr}^{-1}$, we demonstrate
the improvement in the detection efficiency of 26% for stellar-mass BBH mergers
and 16% for intermediate mass black hole binary mergers. To demonstrate the
robustness of the ML-enhanced search for the detection of generic BBH signals,
we show that it has the increased sensitivity to the spin precessing or
eccentric BBH events, even when trained on simulated quasi-circular BBH events
with aligned spins.
