scholarly journals Multiple‐membership multiple‐classification models for social network and group dependences

2013 ◽  
Vol 177 (2) ◽  
pp. 439-455 ◽  
Author(s):  
Mark Tranmer ◽  
David Steel ◽  
William J. Browne
Keyword(s):  
Social Network ◽  
Classification Models ◽  
Multiple Classification ◽  
Multiple Membership

Multiple membership multiple classification (MMMC) models

2001 ◽  
Vol 1 (2) ◽  
pp. 103-124 ◽  
Author(s):  
W.J. Browne ◽  
H. Goldstein ◽  
J. Rasbash
Keyword(s):  
Multiple Classification ◽  
Multiple Membership

Multiple membership multiple classification (MMMC) models

2001 ◽  
Vol 1 (2) ◽  
pp. 103-124 ◽  
Author(s):  
William J Browne ◽  
Harvey Goldstein ◽  
Jon Rasbash
Keyword(s):  
Multiple Classification ◽  
Multiple Membership

scholarly journals A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children’s Physical Activity

2019 ◽  
Vol 16 (24) ◽  
pp. 4889
Author(s):  
Ruth Salway ◽  
Lydia Emm-Collison ◽  
Simon J. Sebire ◽  
Janice L. Thompson ◽  
Deborah A. Lawlor ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Classification Model ◽  
Accelerometer Data ◽  
Individual Level ◽  
Friendship Groups ◽  
Multiple Classification ◽  
Multiple Membership ◽  
Children’S Physical Activity ◽  
Different Levels

Physical activity is influenced by individual, inter-personal and environmental factors. In this paper, we explore the variability in children’s moderate-to-vigorous physical activity (MVPA) at different individual, parent, friend, school and neighbourhood levels. Valid accelerometer data were collected for 1077 children aged 9, and 1129 at age 11, and the average minutes of MVPA were derived for weekdays and weekends. We used a multiple-membership, multiple-classification model (MMMC) multilevel model to compare the variation in physical activity outcomes at each of the different levels. There were differences in the proportion of variance attributable to the different levels between genders, for weekdays and weekends, at ages 9 and 11. The largest proportion of variability in MVPA was attributable to individual variation, accounting for half of the total residual variability for boys, and two thirds of the variability for girls. MVPA clustered within friendship groups, with friends influencing peer MVPA. Including covariates at the different levels explained only small amounts (3%–13%) of variability. There is a need to enhance our understanding of individual level influences on children’s physical activity.

scholarly journals Comparative analysis of multiple classification models to improve PM10 prediction performance

2021 ◽  
Vol 11 (3) ◽  
pp. 2500
Author(s):  
Yong-Jin Jung ◽  
Kyoung-Woo Cho ◽  
Jong-Sung Lee ◽  
Chang-Heon Oh
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Particulate Matter ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Occurrence Rate ◽  
Classification Models ◽  
Comparison Results ◽  
Multiple Classification

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model.

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