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In the multi-messenger astronomy era, accurate sky localization and lowlatency time of gravitational-wave (GW) searches are keys in triggeringsuccessful follow-up observations on the electromagnetic counterpart of GWsignals. We, in this work, focus on the latency time and study the feasibilityof adopting supervised machine learning (ML) method for ranking candidate GWevents. We consider two popular ML methods, random forest and neural networks.We observe that the evaluation time of both methods takes tens of millisecondsfor $\sim$ 45,000 evaluation samples. We compare the classification efficiencybetween the two ML methods and a conventional low-latency search method withrespect to the true positive rate at given false positive rate. The comparisonshows that about 10\% improved efficiency can be achieved at lower falsepositive rate $\sim 2 \times 10^{-5}$ with both ML methods. We also presentthat the search sensitivity can be enhanced by about 18\% at $\sim 10^{-11}$Hzfalse alarm rate. We conclude that adopting ML methods for ranking candidate GWevents is a prospective approach to yield low latency and high efficiency insearches for GW signals from compact binary mergers.

Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers