A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome

S. Dehuri; B. S.P. Mishra; R. Roy; S. B. Cho

doi:10.1145/1980422.1980423

A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome

S. Dehuri, B. S.P. Mishra, R. Roy, S. B. Cho

College of Computing

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

Abstract

This paper presents a serial and parallel genetic based learnable bayesian classifier for designing a prognostic model for metabolic syndrome. The objective of the classifier is to address the fundamental problem of finding the optimal weight in the learnable bayesian classifier, by serial GA, and minimize the response time by parallel GA. The algorithms exhibit an improved capability to eliminate spurious features from the large dataset and aid the researchers in identifying those features that are solely responsible for high prediction accuracy. The effectiveness of the classifier are demonstrated using metabolic syndrome dataset obtained from Yonchon County of Korea.

Original language	English
Title of host publication	Compute 2011 - 4th Annual ACM Bangalore Conference
DOIs	https://doi.org/10.1145/1980422.1980423
Publication status	Published - 2011
Event	4th Annual ACM Bangalore Conference, Compute 2011 - Bangalore, India Duration: 2011 Mar 25 → 2011 Mar 26

Publication series

Name	Compute 2011 - 4th Annual ACM Bangalore Conference

Other

Other	4th Annual ACM Bangalore Conference, Compute 2011
Country/Territory	India
City	Bangalore
Period	11/3/25 → 11/3/26

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1145/1980422.1980423

Cite this

@inproceedings{c600c88ad334438db0fe0e989bce25cd,

title = "A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome",

abstract = "This paper presents a serial and parallel genetic based learnable bayesian classifier for designing a prognostic model for metabolic syndrome. The objective of the classifier is to address the fundamental problem of finding the optimal weight in the learnable bayesian classifier, by serial GA, and minimize the response time by parallel GA. The algorithms exhibit an improved capability to eliminate spurious features from the large dataset and aid the researchers in identifying those features that are solely responsible for high prediction accuracy. The effectiveness of the classifier are demonstrated using metabolic syndrome dataset obtained from Yonchon County of Korea.",

author = "S. Dehuri and Mishra, {B. S.P.} and R. Roy and Cho, {S. B.}",

year = "2011",

doi = "10.1145/1980422.1980423",

language = "English",

isbn = "9781450307505",

series = "Compute 2011 - 4th Annual ACM Bangalore Conference",

booktitle = "Compute 2011 - 4th Annual ACM Bangalore Conference",

note = "4th Annual ACM Bangalore Conference, Compute 2011 ; Conference date: 25-03-2011 Through 26-03-2011",

}

Dehuri, S, Mishra, BSP, Roy, R & Cho, SB 2011, A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome. in Compute 2011 - 4th Annual ACM Bangalore Conference., 1, Compute 2011 - 4th Annual ACM Bangalore Conference, 4th Annual ACM Bangalore Conference, Compute 2011, Bangalore, India, 11/3/25. https://doi.org/10.1145/1980422.1980423

TY - GEN

T1 - A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome

AU - Dehuri, S.

AU - Mishra, B. S.P.

AU - Roy, R.

AU - Cho, S. B.

PY - 2011

Y1 - 2011

N2 - This paper presents a serial and parallel genetic based learnable bayesian classifier for designing a prognostic model for metabolic syndrome. The objective of the classifier is to address the fundamental problem of finding the optimal weight in the learnable bayesian classifier, by serial GA, and minimize the response time by parallel GA. The algorithms exhibit an improved capability to eliminate spurious features from the large dataset and aid the researchers in identifying those features that are solely responsible for high prediction accuracy. The effectiveness of the classifier are demonstrated using metabolic syndrome dataset obtained from Yonchon County of Korea.

AB - This paper presents a serial and parallel genetic based learnable bayesian classifier for designing a prognostic model for metabolic syndrome. The objective of the classifier is to address the fundamental problem of finding the optimal weight in the learnable bayesian classifier, by serial GA, and minimize the response time by parallel GA. The algorithms exhibit an improved capability to eliminate spurious features from the large dataset and aid the researchers in identifying those features that are solely responsible for high prediction accuracy. The effectiveness of the classifier are demonstrated using metabolic syndrome dataset obtained from Yonchon County of Korea.

UR - http://www.scopus.com/inward/record.url?scp=79957998651&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79957998651&partnerID=8YFLogxK

U2 - 10.1145/1980422.1980423

DO - 10.1145/1980422.1980423

M3 - Conference contribution

AN - SCOPUS:79957998651

SN - 9781450307505

T3 - Compute 2011 - 4th Annual ACM Bangalore Conference

BT - Compute 2011 - 4th Annual ACM Bangalore Conference

T2 - 4th Annual ACM Bangalore Conference, Compute 2011

Y2 - 25 March 2011 through 26 March 2011

ER -

A serial and parallel genetic based learning algorithm for Bayesian classifier to predict metabolic syndrome

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this