A two-stage optimized robust kernel density estimation for Bayesian classification with outliers
Chenghao Wei,
Bo Peng,
Chen Li
et al.
Abstract:A Bayesian classifier based on kernel density estimation (KDE) is an effective routine for continuous data classification. However, the presence of outliers and inappropriate selection of bandwidth may cause inaccurate and distorted estimations of the density function. This paper presents an improved Bayesian classification approach by employing an optimized robust kernel density estimation (ORKDE). The impact of outliers can be down-weighted by a two-stage optimization in the conditional probability density c… Show more
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