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Debris disks around stars other than the Sun have received significant attention in studies of exoplanets, specifically exoplanetary system formation. Since debris disks are major sources of infrared emissions, infrared survey data such as the Wide-Field Infrared Survey (WISE) catalog potentially harbors numerous debris disk candidates. However, it is currently challenging to perform disk candidate searches for over 747 million sources in the WISE catalog due to the high probability of false positives caused by interstellar matter, galaxies, and other background artifacts. Crowdsourcing techniques have thus started to harness citizen scientists for debris disk identification since humans can be easily trained to distinguish between desired artifacts and irrelevant noises. With a limited number of citizen scientists, however, increasing data volumes from large surveys will inevitably lead to analysis bottlenecks. To overcome this scalability problem and push the current limits of automated debris disk candidate identification, we present a novel approach that uses citizen science results as a seed to train machine learning based classification. In this paper, we detail a case study with a computer-aided discovery pipeline demonstrating such feasibility based on WISE catalog data and NASA’s Disk Detective project. Our approach of debris disk candidates classification was shown to be robust under a wide range of image quality and features. Our hybrid approach of citizen science with algorithmic scalability can facilitate big data processing for future detections as envisioned in future missions such as the Transiting Exoplanet Survey Satellite (TESS) and the Wide-Field Infrared Survey Telescope (WFIRST). [ABSTRACT FROM AUTHOR]