AI-Enhanced Early Stopping Decision Framework for A/B Testing: A Machine Learning Approach to Optimize Experimental Efficiency
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Abstract
Traditional A/B testing frameworks suffer from inefficiencies in duration management, leading to resource waste and delayed decision-making. This paper presents an AI-enhanced early stopping decision framework that leverages machine learning algorithms to optimize experimental efficiency. Our framework incorporates dynamic threshold adjustment mechanisms and predictive stopping models to reduce testing duration while maintaining statistical rigor. The proposed approach integrates sequential analysis with machine learning techniques, enabling real-time decision-making based on accumulating evidence. Experimental evaluation demonstrates significant improvements in testing efficiency, with average duration reductions of 35% compared to traditional fixed-duration approaches. The framework maintains statistical power while providing robust stopping criteria that adapt to varying experimental conditions. Implementation results across multiple domains validate the practical applicability and scalability of the proposed methodology.
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1. H. Strobelt, et al., "Interactive and visual prompt engineering for ad-hoc task adaptation with large language models," IEEE Trans. Vis. Comput. Graphics, vol. 29, no. 1, pp. 1146-1156, 2022, doi: 10.1109/TVCG.2022.3209479.
2. L. Yang, et al., "Dgrec: Graph neural network for recommendation with diversified embedding generation," in Proc. 16th ACM Int. Conf. Web Search Data Mining, 2023, doi: 10.1145/3539597.3570472.
3. R. Koning, S. Hasan, and A. Chatterji, "Experimentation and start-up performance: Evidence from A/B testing," Manag. Sci., vol. 68, no. 9, pp. 6434-6453, 2022, doi: 10.1287/mnsc.2021.4209.
4. J. G. Greener, et al., "A guide to machine learning for biologists," Nat. Rev. Mol. Cell Biol., vol. 23, no. 1, pp. 40-55, 2022, doi: 10.1038/s41580-021-00407-0.
5. Z. Chen, et al., "iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization," Nucleic Acids Res., vol. 49, no. 10, pp. e60-e60, 2021, doi: 10.1093/nar/gkab122.
6. Y. Jin, et al., "Forecasting building occupancy: A temporal-sequential analysis and machine learning integrated approach," Energy Buildings, vol. 252, p. 111362, 2021, doi: 10.1016/j.enbuild.2021.111362.
7. S. V. Kalinin, et al., "Machine learning for automated experimentation in scanning transmission electron microscopy," npj Comput. Mater., vol. 9, no. 1, p. 227, 2023, doi: 10.1038/s41524-023-01142-0.
8. E. Nichifor, et al., "Eye tracking and an A/B split test for social media marketing optimisation: The connection between the user profile and ad creative components," J. Theor. Appl. Electron. Commer. Res., vol. 16, no. 6, pp. 2319-2340, 2021, doi: 10.3390/jtaer16060128.
9. S. W. Fujo, S. Subramanian, and M. A. Khder, "Customer churn prediction in telecommunication industry using deep learning," Inf. Sci. Lett., vol. 11, no. 1, p. 24, 2022, doi: 10.18576/isl/110120.
10. W. Liu, K. Qian, and S. Zhou, "Algorithmic bias identification and mitigation strategies in machine learning-based credit risk assessment for small and medium enterprises," Ann. Appl. Sci., vol. 5, no. 1, 2024.
11. A. Manickam, et al., "Automated pneumonia detection on chest X-ray images: A deep learning approach with different opti-mizers and transfer learning architectures," Measurement, vol. 184, p. 109953, 2021, doi: 10.1016/j.measurement.2021.109953.
12. L. W. Koblan, et al., "Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning," Nat. Biotechnol., vol. 39, no. 11, pp. 1414-1425, 2021, doi: 10.1038/s41587-021-00938-z.
13. M. Wang and L. Zhu, "Linguistic analysis of verb tense usage patterns in computer science paper abstracts," Acad. Nexus J., vol. 3, no. 3, 2024.
14. M. Sallam, et al., "ChatGPT output regarding compulsory vaccination and COVID-19 vaccine conspiracy: a descriptive study at the outset of a paradigm shift in online search for information," Cureus, vol. 15, no. 2, 2023, doi: 10.7759/cureus.35029.
15. T. Mo, P. Li, and Z. Jiang, "Comparative analysis of large language models' performance in identifying different types of code defects during automated code review," Ann. Appl. Sci., vol. 5, no. 1, 2024.