Open AccessConcept Drift Detection in Dynamic Probabilistic Relational Models
Author(s) -
Nils Finke,
Tanya Braun,
Marcel Gehrke,
Ralf Möller
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128465
Subject(s) - joint probability distribution , probabilistic logic , computer science , probability distribution , factorization , concept drift , encoding (memory) , relational database , stationary distribution , relational model , statistical relational learning , process (computing) , joint (building) , data mining , algorithm , mathematics , artificial intelligence , machine learning , statistics , data stream mining , engineering , markov chain , operating system , architectural engineering
Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.