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Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification
Yung-Kyun Noh · Frank Park · Daniel Lee

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

This paper sheds light on some fundamental connections of the diffusion decision making model of neuroscience and cognitive psychology with k-nearest neighbor classification. We show that conventional k-nearest neighbor classification can be viewed as a special problem of the diffusion decision model in the asymptotic situation. Applying the optimal strategy associated with the diffusion decision model, an adaptive rule is developed for determining appropriate values of k in k-nearest neighbor classification. Making use of the sequential probability ratio test (SPRT) and Bayesian analysis, we propose five different criteria for adaptively acquiring nearest neighbors. Experiments with both synthetic and real datasets demonstrate the effectivness of our classification criteria.

Author Information

Yung-Kyun Noh (Hanyang University / Korea Institute for Advanced Study)
Frank Park (Seoul National University)
Daniel Lee (Samsung Research/Cornell University)

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