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A Comparative Study on Machine Learning Models for Predicting the Behavior of Pet
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반려동물의 행동 예측을 위한 기계학습 모델의 비교 연구

논문 기본 정보

Type
Academic journal
Author
Jaejoon Moon (펫터) Kyuseok Kim (한국폴리텍대학) Moonseon Jeon (한국폴리텍대학) Youngjoon Cho (한국폴리텍대학)
Journal
Korean Institute of Information Technology The Journal of Korean Institute of Information Technology Vol.22 No.9 KCI Accredited Journals
Published
2024.9
Pages
35 - 41 (7page)
DOI
10.14801/jkiit.2024.22.9.35

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A Comparative Study on Machine Learning Models for Predicting the Behavior of Pet
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Abstract· Keywords

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The author"s previous research presented behavior prediction results using three models using four types of labeled behavior data of pet. This study demonstrates that it is possible to label more behavioral data than before and diversify the model. Accordingly, there are a total of 15 types of behavioral data of pet used in this study, and a total of 8 models. As a result of the study, it was found that Random Forest(RF), Gradient Boosting Machine(GBM), and K-Nearest Neighbor(KNN) had the highest accuracy in the order. This result suggests the possibility of predicting the behavior of pet using machine learning-based classification model, and furthermore, it is expected that disease prediction will be possible in the future.
#artificial intelligence #pet technology #behavior prediction #bioinformatics #pet

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 연구 방법
Ⅳ. 연구 결과
Ⅴ. 결론 및 향후 과제
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