CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Bayesian mixed-effects inference on classification performance in hierarchical data sets
Authors
K. H. Brodersen
J. M. Buhmann
+5 more
J. R. Chumbley
J. Daunizeau
Christoph Daniel Mathys
C. S. Ong
K. E. Stephan
Publication date
1 January 2012
Publisher
Doi
Cite
Abstract
Classification algorithms are frequently used on data with a natural hierarchical structure. For instance, classifiers are often trained and tested on trial-wise measurements, separately for each subject within a group. One important question is how classification outcomes observed in individual subjects can be generalized to the population from which the group was sampled. To address this question, this paper introduces novel statistical models that are guided by three desiderata. First, all models explicitly respect the hierarchical nature of the data, that is, they are mixed-effects models that simultaneously account for within-subjects (fixed-effects) and across-subjects (random-effects) variance components. Second, maximum-likelihood estimation is replaced by full Bayesian inference in order to enable natural regularization of the estimation problem and to afford conclusions in terms of posterior probability statements. Third, inference on classification accuracy is complemented by inference on the balanced accuracy, which avoids inflated accuracy estimates for imbalanced data sets. We introduce hierarchical models that satisfy these criteria and demonstrate their advantages over conventional methods usingMCMC implementations for model inversion and model selection on both synthetic and empirical data. We envisage that our approach will improve the sensitivity and validity of statistical inference in future hierarchical classification studies. © 2012
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
SISSA Digital Library
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:iris.sissa.it:20.500.11767...
Last time updated on 01/01/2018
ZORA
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:www.zora.uzh.ch:71594
Last time updated on 09/07/2013
Sissa Digital Library
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:iris.sissa.it:20.500.11767...
Last time updated on 26/02/2020