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Summary: A Weight-Adjusted Voting Algorithm for Ensemble of
Classifiers
Hyunjoong Kima,
, Hyeuk Kimb
, Hojin Moonc
, Hongshik Ahnb
a
Department of Applied Statistics, Yonsei University, Seoul 120-749, South Korea
b
Department of Applied Math and Statistics, Stony Brook University, Stony Brook, NY
11794-3600
c
Department of Mathematics and Statistics, California State University, Long Beach, CA
90840-1001
Abstract
We present a new weighted voting classification ensemble method, called WAVE,
that uses two weight vectors: a weight vector of classifiers and a weight vector of
instances. The instance weight vector assigns higher weights to observations that
are hard to classify. The weight vector of classifiers puts larger weights on classifiers
that perform better on hard-to-classify instances. One weight vector is designed to
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