# Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation

@article{Chen2019FastMP, title={Fast Multi-Class Probabilistic Classifier by Sparse Non-parametric Density Estimation}, author={Wan-Ping Chen and Yuan-chin Ivan Chang}, journal={ArXiv}, year={2019}, volume={abs/1901.01000} }

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…

## Figures, Tables, and Topics from this paper

## References

SHOWING 1-10 OF 36 REFERENCES

Sparse linear discriminant analysis by thresholding for high dimensional data

- Mathematics
- 2011

In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the…

Classification Using Kernel Density Estimates

- Mathematics, Computer ScienceTechnometrics
- 2006

A multiscale approach along with a graphical device leading to a more informative discriminant analysis than the usual approach based on a single optimum scale of smoothing for each class density estimate when there are more than two competing classes is presented.

Algorithms for maximum-likelihood bandwidth selection in kernel density estimators

- Mathematics, Computer SciencePattern Recognit. Lett.
- 2012

The fixed-point algorithms proposed obtain the maximum likelihood bandwidth in few iterations, without performing an exhaustive bandwidth search, which is unfeasible in the multivariate case.

High Dimensional Classification Using Features Annealed Independence Rules.

- Mathematics, MedicineAnnals of statistics
- 2008

The conditions under which all the important features can be selected by the two-sample t-statistic are established and the choice of the optimal number of features, or equivalently, the threshold value of the test statistics are proposed based on an upper bound of the classification error.

An Unsupervised and Nonparametric Classification Procedure Based on Mixtures with Known Weights

- Mathematics
- 1998

Abstract I consider a new problem of classification into n(n ≥ 2) disjoint classes based on features of unclassified data. It is assumed that the data are grouped into m(M ≥ n) disjoint sets and…

Minimax nonparametric classification - Part II: Model selection for adaptation

- Mathematics, Computer ScienceIEEE Trans. Inf. Theory
- 1999

It is shown that with a suitable model selection criterion, the penalized maximum-likelihood estimator has a risk bounded by an index of resolvability expressing a good tradeoff among approximation error, estimation error, and model complexity.

Multiple-resolution classification with combination of density estimators

- Mathematics, Computer ScienceConnect. Sci.
- 2011

The tests show that the introduced algorithm is superior to the basic version based on one estimator per class, and the proposed practical implementation of such an algorithm with parameters of density estimators adjusted to minimise the misclassification probability is proposed.

Estimating Continuous Distributions in Bayesian Classifiers

- Computer Science, MathematicsUAI
- 1995

This paper abandon the normality assumption and instead use statistical methods for nonparametric density estimation for kernel estimation, which suggests that kernel estimation is a useful tool for learning Bayesian models.

NONPARAMETRIC DENSITY ESTIMATION IN HIGH-DIMENSIONS

- Mathematics
- 2013

Penalized likelihood density estimation provides an effective approach to the nonparametric fitting of graphical models, with conditional independence struc- tures characterized via selective term…

Consistency of Data-driven Histogram Methods for Density Estimation and Classification

- Mathematics
- 1996

We present general sufficient conditions for the almost sureL1-consistency of histogram density estimates based on data-dependent partitions. Analogous conditions guarantee the almost-sure risk…