1 research outputs found
Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation
The model interpretation is essential in many application scenarios and to
build a classification model with a ease of model interpretation may provide
useful information for further studies and improvement. It is common to
encounter with a lengthy set of variables in modern data analysis, especially
when data are collected in some automatic ways. This kinds of datasets may not
collected with a specific analysis target and usually contains redundant
features, which have no contribution to a the current analysis task of
interest. Variable selection is a common way to increase the ability of model
interpretation and is popularly used with some parametric classification
models. There is a lack of studies about variable selection in nonparametric
classification models such as the density estimation-based methods and this is
especially the case for multiple-class classification situations. In this study
we study multiple-class classification problems using the thought of sparse
non-parametric density estimation and propose a method for identifying high
impacts variables for each class. We present the asymptotic properties and the
computation procedure for the proposed method together with some suggested
sample size. We also repost the numerical results using both synthesized and
some real data sets