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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.3
- 발행연도
- 2023.6
- 수록면
- 252 - 260 (9page)
- DOI
- 10.5573/IEIESPC.2023.12.3.252
이용수
초록· 키워드
오류제보하기
Automated machine-learning technology can achieve the automation, efficiency, and intelligence of machine learning. This technology can lower the application threshold of artificial intelligence (AI) and has attracted academic attention. Therefore, taking a typical classification problem as an example, this study proposes a framework of a machine-learning pipeline automation design algorithm combining a Bayesian algorithm and reinforcement learning. Aiming at the CASH problem, the study divides the machine-learning pipeline design problem into two sub-problems. One is to realize a machine-learning pipeline structure search based on reinforcement learning. The other is to realize machine-learning pipeline hyperparameter optimization configurations based on the Bayesian network model. The experimental results showed that when the time budget was four hours, the average balanced accuracy of Auto-PLD(Ed note: You need to define what these are in the abstract.) was 0.003 higher than that of Auto-PLD-random and 0.001 higher than that of Auto-sklearn. The success rate of the Auto-PLD machine-learning pipeline evaluation on various datasets exceeded 92%. Based on the Bayesian model and reinforcement learning, the machine-learning pipeline automation design algorithm framework can also play a good role in practical applications. Moreover, it can promote the development of artificial intelligence technology.
목차
Abstract
1. Introduction
1. Related Works
2. Construction of Auto-PLD Algorithm Framework for Classic Scenes
3. Performance Evaluation of the Auto-PLD Algorithm
4. Conclusion
5. Fundings
Reference
참고문헌 (20)
참고문헌 신청
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Gupta. G, Katarya. R. “EnPSO: An AutoML technique for generating ensemble recommender system,” Arabian Journal for Science and Engineering, vol. 46, no. 9, pp. 8677-8695, 2021. Article (CrossRef Link)
Roman. D, Saxena. S, Robu. V, Pecht. M, Flynn. D. “Machine learning pipeline for battery state-of-health estimation,” Nature Machine Intelligence, vol. 3, no. 5, pp. 447-456, 2021. Article (CrossRef Link)
Ajirlou. AF, Partin-Vaisband. I. “A machine learning pipeline stage for adaptive frequency adjustment,” IEEE Transactions on Computers, vol. 71, no. 3, pp. 587-598, 2021. Article (CrossRef Link)
Tan. HB, Xiong. F, Jiang. YL, Huang. WC, Wang. Y, Li. HH, You. T, Fu. TT, Peng. B W. “The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia,” Scientific reports,
Gupta. G, Katarya. R. “EnPSO: An AutoML technique for generating ensemble recommender system,” Arabian Journal for Science and Engineering, vol. 46, no. 9, pp. 8677-8695, 2021. Article (CrossRef Link)
Roman. D, Saxena. S, Robu. V, Pecht. M, Flynn. D. “Machine learning pipeline for battery state-of-health estimation,” Nature Machine Intelligence, vol. 3, no. 5, pp. 447-456, 2021. Article (CrossRef Link)
Ajirlou. AF, Partin-Vaisband. I. “A machine learning pipeline stage for adaptive frequency adjustment,” IEEE Transactions on Computers, vol. 71, no. 3, pp. 587-598, 2021. Article (CrossRef Link)
Tan. HB, Xiong. F, Jiang. YL, Huang. WC, Wang. Y, Li. HH, You. T, Fu. TT, Peng. B W. “The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia,” Scientific reports,
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