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Rapid Spline-based Kernel Density Estimation for Bayesian Networks

9 years 6 months ago
Rapid Spline-based Kernel Density Estimation for Bayesian Networks
The likelihood for patterns of continuous attributes for the naive Bayesian classifier (NBC) may be approximated by kernel density estimation (KDE), letting every pattern influence the shape of the probability density thus leading to accurate estimation. KDE suffers from computational cost making it unpractical in many real-world applications. We smooth the density using a spline thus requiring only very few coefficients for the estimation rather than the whole training set, allowing rapid implementation of the NBC without sacrificing classifier accuracy. Experiments conducted over several real-world databases reveal acceleration, sometimes in several orders of magnitude, in favor of the spline approximation making the application of KDE to the NBC practical.
Boaz Lerner, Yaniv Gurwicz
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Boaz Lerner, Yaniv Gurwicz
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