Active Learning for Planet Habitability Classification under Extreme Class Imbalance

ArXi:2602.23666v2 Announce Type: replace-cross The increasing size and heterogeneity of exoplanet catalogs have made systematic habitability assessment challenging, particularly given the extreme scarcity of potentially habitable planets and the evolving nature of their labels. In this study, we explore the use of pool-based active learning to improve the efficiency of habitability classification under realistic observational constraints.