Poster #17 - Applying AdaBoost to Improve Diagnostic Accuracy: A Simulation Study

• In Event: 79.052 - MTCC Poster Session 19
  In Poster Session: 79.052-2 - Educational Measurement, Psychometrics, and Assessment Poster Session

Abstract

Cognitive diagnostic modeling has been adopted to support various diagnostic measuring processes. However, the diagnostic information provided by traditional estimation approaches often suffers from low accuracy, especially under small sample conditions. This paper adopts an AdaBoost technique estimate latent variables. The proposed approach involves the construction of a simple iterative algorithm that is based upon the AdaBoost technique such that the area under the curve (AUC) is minimized. The algorithmic details are elaborated via pseudo codes with line-to-line verbal explanations. Simulation studies were conducted such that the improvement of latent variable estimates can be examined. As a result, the function of minimizing AUC with an AdaBoost technique can replace traditional EM-based estimation approaches to yield more trustworthy diagnostic results.

Author

• ZHEHAN JIANG, Baylor College of Medicine