Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children

Toshiaki Aida; Chiyori Haga

doi:10.1109/ICBDA.2019.8713200

Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children

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

Abstract

The time developments of BMIs (Body Math Index) of children are known to be classified into several types, and the results can be utilized to their health guidance. For this purpose, we approach to the classification problem of the time developments of the BMI data of Japanese children from their babyhood to junior high school age. We have inferred the dimension of their principal component space and the number of component distributions of a Gaussian mixture model, adopting a framework of variational Bayesian statistical inference. As a result, the data are found to be classified into 8 types in a 12-dimensional principal component space.

Original language	English
Title of host publication	2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	21-25
Number of pages	5
ISBN (Electronic)	9781728112824
DOIs	https://doi.org/10.1109/ICBDA.2019.8713200
Publication status	Published - May 10 2019
Event	4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China Duration: Mar 15 2019 → Mar 18 2019

Publication series

Name	2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Conference

Conference	4th IEEE International Conference on Big Data Analytics, ICBDA 2019
Country/Territory	China
City	Suzhou
Period	3/15/19 → 3/18/19

Keywords

BMI
Bayes
Classification
GMM
PCA
time series
variational approximation

ASJC Scopus subject areas

Artificial Intelligence
Information Systems
Information Systems and Management
Statistics, Probability and Uncertainty

Access to Document

10.1109/ICBDA.2019.8713200

Cite this

Aida, T., & Haga, C. (2019). Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children. In 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 (pp. 21-25). [8713200] (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA.2019.8713200

Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children. / Aida, Toshiaki; Haga, Chiyori.

2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 21-25 8713200 (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019).

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

Aida, T & Haga, C 2019, Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children. in 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019., 8713200, 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019, Institute of Electrical and Electronics Engineers Inc., pp. 21-25, 4th IEEE International Conference on Big Data Analytics, ICBDA 2019, Suzhou, China, 3/15/19. https://doi.org/10.1109/ICBDA.2019.8713200

Aida T, Haga C. Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children. In 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 21-25. 8713200. (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019). https://doi.org/10.1109/ICBDA.2019.8713200

Aida, Toshiaki ; Haga, Chiyori. / Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children. 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 21-25 (2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019).

@inproceedings{1591d6c071c2464588fed60ebaf1a2ad,

title = "Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children",

abstract = "The time developments of BMIs (Body Math Index) of children are known to be classified into several types, and the results can be utilized to their health guidance. For this purpose, we approach to the classification problem of the time developments of the BMI data of Japanese children from their babyhood to junior high school age. We have inferred the dimension of their principal component space and the number of component distributions of a Gaussian mixture model, adopting a framework of variational Bayesian statistical inference. As a result, the data are found to be classified into 8 types in a 12-dimensional principal component space.",

keywords = "BMI, Bayes, Classification, GMM, PCA, time series, variational approximation",

author = "Toshiaki Aida and Chiyori Haga",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number JP18K11021. Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th IEEE International Conference on Big Data Analytics, ICBDA 2019 ; Conference date: 15-03-2019 Through 18-03-2019",

year = "2019",

month = may,

day = "10",

doi = "10.1109/ICBDA.2019.8713200",

language = "English",

series = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",

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

pages = "21--25",

booktitle = "2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019",

}

TY - GEN

T1 - Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children

AU - Aida, Toshiaki

AU - Haga, Chiyori

PY - 2019/5/10

Y1 - 2019/5/10

N2 - The time developments of BMIs (Body Math Index) of children are known to be classified into several types, and the results can be utilized to their health guidance. For this purpose, we approach to the classification problem of the time developments of the BMI data of Japanese children from their babyhood to junior high school age. We have inferred the dimension of their principal component space and the number of component distributions of a Gaussian mixture model, adopting a framework of variational Bayesian statistical inference. As a result, the data are found to be classified into 8 types in a 12-dimensional principal component space.

AB - The time developments of BMIs (Body Math Index) of children are known to be classified into several types, and the results can be utilized to their health guidance. For this purpose, we approach to the classification problem of the time developments of the BMI data of Japanese children from their babyhood to junior high school age. We have inferred the dimension of their principal component space and the number of component distributions of a Gaussian mixture model, adopting a framework of variational Bayesian statistical inference. As a result, the data are found to be classified into 8 types in a 12-dimensional principal component space.

KW - BMI

KW - Bayes

KW - Classification

KW - GMM

KW - PCA

KW - time series

KW - variational approximation

UR - http://www.scopus.com/inward/record.url?scp=85066602374&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066602374&partnerID=8YFLogxK

U2 - 10.1109/ICBDA.2019.8713200

DO - 10.1109/ICBDA.2019.8713200

M3 - Conference contribution

AN - SCOPUS:85066602374

T3 - 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

SP - 21

EP - 25

BT - 2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Y2 - 15 March 2019 through 18 March 2019

ER -

Bayesian Approach to the Classification of BMI Time Series Data from Babyhood to Junior High School Age of Japanese Children

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this