Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that often persists into adulthood. However, as lacking objective measures, several studies have questioned the stability in diagnosing of ADHD from childhood to adulthood. In this study, we propose a novel feature selection framework based on functional connectivity (FCs) pattern, the so-called `FS_RIWEL,' which could classify ADHD from agematched healthy controls (HCs) with ~80% accuracy (both for children and adults). More importantly, the feature space learned from child ADHD dataset can discriminate adult ADHD from HCs at ~70% accuracy. To the best of our knowledge, this is the first attempt to perform a cross-cohort prediction between the adult and child ADHD using FC features. In addition, the most frequently selected FCs indicate that ADHD exhibit widely-impaired FC patterns in frontoparietal, basal ganglia, cerebellum network and so on suggesting that FCs may serve as potential biomarkers for ADHD diagnosis.