A simple Bayesian algorithm for feature ranking in high dimensional regression problems

Enes Makalic, Daniel F. Schmidt

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Variable selection or feature ranking is a problem of fundamental importance in modern scientific research where data sets comprising hundreds of thousands of potential predictor features and only a few hundred samples are not uncommon. This paper introduces a novel Bayesian algorithm for feature ranking (BFR) which does not require any user specified parameters. The BFR algorithm is very general and can be applied to both parametric regression and classification problems. An empirical comparison of BFR against random forests and marginal covariate screening demonstrates promising performance in both real and artificial experiments.

Original languageEnglish
Title of host publicationAI 2011
Subtitle of host publicationAdvances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings
PublisherSpringer
Pages223-230
Number of pages8
ISBN (Print)9783642258312
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventAustralasian Joint Conference on Artificial Intelligence 2011 - Perth, Australia
Duration: 5 Dec 20118 Dec 2011
Conference number: 24th
https://link.springer.com/book/10.1007/978-3-642-25832-9 (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7106
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAustralasian Joint Conference on Artificial Intelligence 2011
Abbreviated titleAI 2011
CountryAustralia
CityPerth
Period5/12/118/12/11
Internet address

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