Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object. These groups are mainly identified in proper elements or frequencies domains. Because of robotic telescope surveys, the number of known asteroids has increased from
about 10,000 in the early 90’s to more than 750,000. Traditional approaches for identifying new members of asteroid families, like the hierarchical
clustering method (HCM), may struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches. The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to retrieve up to 97% of family members identified with standard HCM.