@Article{cmc.2020.011632,
AUTHOR = {Yunfan Ye, Fang Liu, Shan Zhao, Wanting Hu, Zhiyao Liang},
TITLE = {Ensemble Learning Based on GBDT and CNN for Adoptability  Prediction},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1361--1372},
URL = {http://www.techscience.com/cmc/v65n2/39879},
ISSN = {1546-2226},
ABSTRACT = {By efficiently and accurately predicting the adoptability of pets, shelters and 
rescuers can be positively guided on improving attraction of pet profiles, reducing animal 
suffering and euthanization. Previous prediction methods usually only used a single type 
of content for training. However, many pets contain not only textual content, but also 
images. To make full use of textual and visual information, this paper proposed a novel 
method to process pets that contain multimodal information. We employed several CNN
(Convolutional Neural Network) based models and other methods to extract features from 
images and texts to obtain the initial multimodal representation, then reduce the dimensions 
and fuse them. Finally, we trained the fused features with two GBDT (Gradient Boosting 
Decision Tree) based models and a Neural Network (NN) and compare the performance of 
them and their ensemble. The evaluation result demonstrates that the proposed ensemble 
learning can improve the accuracy of prediction.},
DOI = {10.32604/cmc.2020.011632}
}