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Ensemble Learning Based on GBDT and CNN for Adoptability Prediction

Yunfan Ye1, Fang Liu1, *, Shan Zhao2, Wanting Hu3, Zhiyao Liang4

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.

* Corresponding Author: Fang Liu. Email: email.

Computers, Materials & Continua 2020, 65(2), 1361-1372. https://doi.org/10.32604/cmc.2020.011632

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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Cite This Article

Y. Ye, F. Liu, S. Zhao, W. Hu and Z. Liang, "Ensemble learning based on gbdt and cnn for adoptability prediction," Computers, Materials & Continua, vol. 65, no.2, pp. 1361–1372, 2020.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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