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 proceedingConference 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 languageEnglish
Title of host publication2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages21-25
Number of pages5
ISBN (Electronic)9781728112824
DOIs
Publication statusPublished - May 10 2019
Event4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China
Duration: Mar 15 2019Mar 18 2019

Publication series

Name2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Conference

Conference4th IEEE International Conference on Big Data Analytics, ICBDA 2019
CountryChina
CitySuzhou
Period3/15/193/18/19

Fingerprint

Principal Components
Time Series Data
Bayesian Approach
Time series
Health
Gaussian Mixture Model
Number of Components
Statistical Inference
Classification Problems
Guidance
Children
Time series data
Bayesian approach
Principal components
High school
Framework
Statistical inference
Gaussian mixture model

Keywords

  • Bayes
  • BMI
  • Classification
  • GMM
  • PCA
  • time series
  • variational approximation

ASJC Scopus subject areas

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

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 proceedingConference 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 = "Bayes, BMI, Classification, GMM, PCA, time series, variational approximation",
author = "Toshiaki Aida and Chiyori Haga",
year = "2019",
month = "5",
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 - Bayes

KW - BMI

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

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.

ER -