PDF] Human Body Shape Prediction and Analysis Using Predictive Clustering Tree

This work proposes to use Predictive Clustering Trees (PCT) to predict human body shapes from demographic information and demonstrates empirically that the PCT-based method has similar predicting power as the numerical approaches using body measurements. Predictive modeling aims at constructing models that predict a target property of an object based on its descriptions. In digital human modeling, it can be applied to predicting human body shape from images, measurements, or descriptive features. While images and measurements can be converted to numerical values, it is difficult to assign numerical values to descriptive features and therefore regression based methods cannot be applied. In this work, we propose to use Predictive Clustering Trees (PCT) to predict human body shapes from demographic information. We build PCTs using a dataset of demographic attributes and body shape descriptors. We demonstrate empirically that the PCT-based method has similar predicting power as the numerical approaches using body measurements. The PCTs also reveal interesting structures of the training dataset and provide interpretations of the body shape variations from the perspective of the demographic attributes.

Clustering predicted structures at the scale of the known protein

Human disease prediction from microbiome data by multiple feature

Analysis of missense variants in the human genome reveals

Drug–target interaction prediction based on protein features

Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Applied Sciences, Free Full-Text

Towards Explainability of Machine Learning Models in Insurance

Frontiers Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids

Electronics, Free Full-Text

Silhouette images enable estimation of body fat distribution and