Optimal Bayesian classification in nonstationary streaming environments

Jehandad Khan; Nidhal Bouaynaya; Robi Polikar

doi:10.1109/IJCNN.2014.6889924

Optimal Bayesian classification in nonstationary streaming environments

Jehandad Khan, Nidhal Bouaynaya, Robi Polikar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A novel method of classifying data drawn from a nonstationary distribution with drifting mean and variance is presented. The novelty of the approach is based on splitting the problem of tracking a nonstationary distribution into separate classification and time series state estimation problems. State space models for drift in both the mean and variance are presented, which are then successfully tracked using a Kaiman filter and a particle filter for the linear and non-linear parts respectively. Preliminary results, which show the promising potential of the approach, are also presented, along with concluding remarks for potential uses of the proposed approach.

Original language	English (US)
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	609-616
Number of pages	8
ISBN (Electronic)	9781479914845
DOIs	https://doi.org/10.1109/IJCNN.2014.6889924
State	Published - Sep 3 2014
Event	2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China Duration: Jul 6 2014 → Jul 11 2014

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Other

Other	2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/Territory	China
City	Beijing
Period	7/6/14 → 7/11/14

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2014.6889924

Cite this

Khan, J., Bouaynaya, N., & Polikar, R. (2014). Optimal Bayesian classification in nonstationary streaming environments. In Proceedings of the International Joint Conference on Neural Networks (pp. 609-616). Article 6889924 (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2014.6889924

@inproceedings{8999fbcd9d47478a9f46fe78777d8b13,

title = "Optimal Bayesian classification in nonstationary streaming environments",

abstract = "A novel method of classifying data drawn from a nonstationary distribution with drifting mean and variance is presented. The novelty of the approach is based on splitting the problem of tracking a nonstationary distribution into separate classification and time series state estimation problems. State space models for drift in both the mean and variance are presented, which are then successfully tracked using a Kaiman filter and a particle filter for the linear and non-linear parts respectively. Preliminary results, which show the promising potential of the approach, are also presented, along with concluding remarks for potential uses of the proposed approach.",

author = "Jehandad Khan and Nidhal Bouaynaya and Robi Polikar",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

month = sep,

day = "3",

doi = "10.1109/IJCNN.2014.6889924",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "609--616",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

address = "United States",

}

Khan, J, Bouaynaya, N & Polikar, R 2014, Optimal Bayesian classification in nonstationary streaming environments. in Proceedings of the International Joint Conference on Neural Networks., 6889924, Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., pp. 609-616, 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, 7/6/14. https://doi.org/10.1109/IJCNN.2014.6889924

Optimal Bayesian classification in nonstationary streaming environments. / Khan, Jehandad; Bouaynaya, Nidhal ; Polikar, Robi.
Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., 2014. p. 609-616 6889924 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Optimal Bayesian classification in nonstationary streaming environments

AU - Khan, Jehandad

AU - Bouaynaya, Nidhal

AU - Polikar, Robi

PY - 2014/9/3

Y1 - 2014/9/3

N2 - A novel method of classifying data drawn from a nonstationary distribution with drifting mean and variance is presented. The novelty of the approach is based on splitting the problem of tracking a nonstationary distribution into separate classification and time series state estimation problems. State space models for drift in both the mean and variance are presented, which are then successfully tracked using a Kaiman filter and a particle filter for the linear and non-linear parts respectively. Preliminary results, which show the promising potential of the approach, are also presented, along with concluding remarks for potential uses of the proposed approach.

AB - A novel method of classifying data drawn from a nonstationary distribution with drifting mean and variance is presented. The novelty of the approach is based on splitting the problem of tracking a nonstationary distribution into separate classification and time series state estimation problems. State space models for drift in both the mean and variance are presented, which are then successfully tracked using a Kaiman filter and a particle filter for the linear and non-linear parts respectively. Preliminary results, which show the promising potential of the approach, are also presented, along with concluding remarks for potential uses of the proposed approach.

UR - http://www.scopus.com/inward/record.url?scp=84908472534&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908472534&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2014.6889924

DO - 10.1109/IJCNN.2014.6889924

M3 - Conference contribution

AN - SCOPUS:84908472534

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 609

EP - 616

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 International Joint Conference on Neural Networks, IJCNN 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

Optimal Bayesian classification in nonstationary streaming environments

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this