INDICISIVE CLASSIFIER (FriAmPO1)

Author(s) : Zhenqiu Zhang (University of Illinois at Urbana-Champaign, United States of America) Xun Xu (University of Illinois at Urbana-Champaign, United States of America) Thomas Huang (University of Illinois at Urbana-Champaign, United States of America)

Abstract : Nearest neighbor classification expects the class conditional probabilities to be locally constant. The assumption becomes invalid in high dimension due to the curse-of-dimensionality. Severe bias can be introduced under this condition when using nearest neighbor rule. We propose an adaptive nearest neighbor classification method ¡°indecisive classifier¡± to minimize bias and variance by avoiding decision making in some hard-decision region. As a result, better classification performance can be expected in some scenario such as video based face recognition.

Menu