Marginal and simultaneous predictive classification using stratified graphical models

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Henrik Nyman, Jie Xiong, Johan Pensar, Jukka Corander
Publication year: 2015
Journal: Advances in Data Analysis and Classification
Start page: 1
End page: 22


Abstract

An inductive probabilistic classification rulemust generally obey the principles
of Bayesian predictive inference, such that all observed and unobserved stochastic
quantities are jointly modeled and the parameter uncertainty is fully acknowledged
through the posterior predictive distribution. Several such rules have been recently considered
and their asymptotic behavior has been characterized under the assumption
that the observed features or variables used for building a classifier are conditionally
independent given a simultaneous labeling of both the training samples and those
from an unknown origin. Here we extend the theoretical results to predictive classifiers
acknowledging feature dependencies either through graphical models or sparser
alternatives defined as stratified graphical models. We show through experimentation
with both synthetic and real data that the predictive classifiers encoding dependencies
have the potential to substantially improve classification accuracy compared with both
standard discriminative classifiers and the predictive classifiers based on solely conditionally
independent features. In most of our experiments stratified graphical models
show an advantage over ordinary graphical models.
An inductive probabilistic classification rule must generally obey the principles
of Bayesian predictive inference, such that all observed and unobserved stochastic
quantities are jointly modeled and the parameter uncertainty is fully acknowledged
through the posterior predictive distribution. Several such rules have been recently considered and their asymptotic behavior has been characterized under the assumption that the observed features or variables used for building a classifier are conditionally independent given a simultaneous labeling of both the training samples and those from an unknown origin. Here we extend the theoretical results to predictive classifiers acknowledging feature dependencies either through graphical models or sparser alternatives defined as stratified graphical models. We show through experimentation with both synthetic and real data that the predictive classifiers encoding dependencies have the potential to substantially improve classification accuracy compared with both standard discriminative classifiers and the predictive classifiers based on solely conditionally independent features. In most of our experiments stratified graphical models show an advantage over ordinary graphical models.

Last updated on 2019-14-10 at 03:15