Ensemble Learning Based on GBDT and CNN for Adoptability Prediction |
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Authors: | Yunfan Ye Fang Liu Shan Zhao Wanting Hu Zhiyao Liang |
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Affiliation: | 1.School of Design, Hunan University, Changsha, 410082, China.
2 College of Computer, National University of Defense Technology, Changsha, 410073, China.
3 Canvard College, Beijing Technology and Business University, Beijing, 100037, China.
4 School of Science and Technology, Macau University, 999078, Macau. |
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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. |
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Keywords: | Adoptability of pets multimodal representation CNN GBDT ensemble learning |
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