loading
Documents

Research.Publish.Connect.

Paper

Authors: Hatim Alsuwat ; Emad Alsuwat ; Marco Valtorta ; John Rose and Csilla Farkas

Affiliation: Department of Computer Science and Engineering, University of South Carolina, Columbia, SC and U.S.A.

ISBN: 978-989-758-382-7

ISSN: 2184-3228

Keyword(s): Concept Drift, Concept Drift Detection, Nonstationary Environments, Bayesian Networks, Latent Variables.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: Concept drift is a significant challenge that greatly influences the accuracy and reliability of machine learning models. There is, therefore, a need to detect concept drift in order to ensure the validity of learned models. In this research, we study the issue of concept drift in the context of discrete Bayesian networks. We propose a probabilistic graphical model framework to explicitly detect the presence of concept drift using latent variables. We employ latent variables to model real concept drift and uncertainty drift over time. For modeling real concept drift, we propose to monitor the mean of the distribution of the latent variable over time. For modeling uncertainty drift, we suggest to monitor the change in beliefs of the latent variable over time, i.e., we monitor the maximum value that the probability density function of the distribution takes over time. We implement our proposed framework and present our empirical results using two of the most commonly used Bayesian netwo rks in Bayesian experiments, namely the Burglary-Earthquake Network and the Chest Clinic network. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.237.94.109

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Alsuwat, H.; Alsuwat, E.; Valtorta, M.; Rose, J. and Farkas, C. (2019). Modeling Concept Drift in the Context of Discrete Bayesian Networks.In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-382-7, ISSN 2184-3228, pages 214-224. DOI: 10.5220/0008384702140224

@conference{kdir19,
author={Hatim Alsuwat. and Emad Alsuwat. and Marco Valtorta. and John Rose. and Csilla Farkas.},
title={Modeling Concept Drift in the Context of Discrete Bayesian Networks},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2019},
pages={214-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008384702140224},
isbn={978-989-758-382-7},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Modeling Concept Drift in the Context of Discrete Bayesian Networks
SN - 978-989-758-382-7
AU - Alsuwat, H.
AU - Alsuwat, E.
AU - Valtorta, M.
AU - Rose, J.
AU - Farkas, C.
PY - 2019
SP - 214
EP - 224
DO - 10.5220/0008384702140224

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.