Modeling concept drift: A probabilistic graphical model based approach

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

Abstrakt

An often used approach for detecting and adapting to concept drift when doing classification is to treat the data as i.i.d. and use changes in classification accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure efficient inference and learning, we re- sort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real financial data set from a Spanish bank.
Luk

Detaljer

An often used approach for detecting and adapting to concept drift when doing classification is to treat the data as i.i.d. and use changes in classification accuracy as an indication of concept drift. In this paper, we take a different perspective and propose a framework, based on probabilistic graphical models, that explicitly represents concept drift using latent variables. To ensure efficient inference and learning, we re- sort to a variational Bayes inference scheme. As a proof of concept, we demonstrate and analyze the proposed framework using synthetic data sets as well as a real financial data set from a Spanish bank.
OriginalsprogEngelsk
TitelAdvances in Intelligent Data Analysis XIV : 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings
RedaktørerElisa Fromont, Tijl De Bie, Matthijs van Leeuwen
UdgiverSpringer
Publikationsdato2015
Sider72-83
ISBN (trykt)978-3-319-24464-8
ISBN (elektronisk)978-3-319-24465-5
DOI
StatusUdgivet - 2015
Begivenhed - Saint Etienne, Frankrig

Konference

KonferenceInternational Symposium, IDA 2015
Nummer14th
LandFrankrig
BySaint Etienne
Periode22/10/201524/10/2015
SerieLecture Notes in Computer Science
Nummer9385
ISSN0302-9743

Download-statistik

Ingen data tilgængelig
ID: 217708736