Automated data-driven mining and learning of adoption prediction model in the United States

Yinuo Liang

doi:10.1117/12.2627583

22 April 2022 Automated data-driven mining and learning of adoption prediction model in the United States

Yinuo Liang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121632B (2022) https://doi.org/10.1117/12.2627583
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Animal shelters work around-the-clock to help pets be rescued across the country. In most cases, people think that animal adoption is mainly driven by intuition and emotion. A family comes in, falls in love, and then welcomes a new pet into their home. Through analysis of the data, we identified the features which best predict whether an animal will be adopted. After digging into the data, and trying to understand the physical characteristics of each animal, it is obvious that the data is interesting and requires some simple feature-engineering. Overall, our proposed mathematical model can effectively help improve the adoption performance in practice, and help the animal shelter improve their efficiency, and reach a higher adoption rate. The proposed method was evaluated using major-quantitative measurements of machine learning techniques, and we concluded that the established framework can be implemented to automatically classify the small animals into multiple categories so they can be taken care of to save human labor, and fundamentally, will contribute to the effective and efficient small-animal protection.

Citation Download Citation

Yinuo Liang "Automated data-driven mining and learning of adoption prediction model in the United States", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121632B (22 April 2022); https://doi.org/10.1117/12.2627583

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