集合深度学习和机器学习:应用、机遇、挑战和未来方向

N. Rane, Saurabh Choudhary, Jayesh Rane
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引用次数: 0

摘要

集合深度学习和机器学习的融合已成为应对医疗保健、金融和自主系统等不同领域复杂挑战的重要策略。集合方法结合了多个模型的优势,以提高预测准确性、鲁棒性和普适性而著称。本文研究了集合技术的应用,强调其在提高医疗成像诊断精度、推进金融服务欺诈检测机制以及完善自动驾驶汽车决策方面的作用。集合方法(包括堆叠、提升和装袋)的最新进展表明,在各种情况下,其性能优于单一模型。然而,在集合学习带来机遇的同时,也面临着一些挑战,如计算要求高、模型可解释性问题以及过度拟合的可能性。本研究探讨了应对这些挑战的方法,包括创建更高效的算法,并纳入可解释人工智能(XAI)框架,以提高透明度和用户信任度。此外,我们还讨论了量子计算和联合学习等尖端技术对集合技术发展的未来影响。大数据的激增、计算硬件的进步以及对实时、可扩展解决方案的需求,将决定集合深度学习和机器学习的未来。本文广泛回顾了集合学习的现状,指出了重大挑战,并提出了未来的研究方向,以充分利用这些技术的潜力解决现实世界中的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ensemble Deep Learning and Machine Learning: Applications, Opportunities, Challenges, and Future Directions
The convergence of ensemble deep learning and machine learning has become a critical strategy for tackling intricate challenges across diverse fields such as healthcare, finance, and autonomous systems. Ensemble approaches, which combine the strengths of multiple models, are known for enhancing predictive accuracy, robustness, and generalizability. This paper investigates the applications of ensemble techniques, emphasizing their role in improving diagnostic precision in medical imaging, advancing fraud detection mechanisms in financial services, and refining decision-making in autonomous vehicles. Recent advancements in ensemble methods, including stacking, boosting, and bagging, have shown to outperform single models in various contexts. However, several challenges accompany the opportunities offered by ensemble learning, such as high computational demands, issues with model interpretability, and the potential for overfitting. This study explores ways to address these challenges, including the creation of more efficient algorithms and the incorporation of explainable AI (XAI) frameworks to enhance transparency and user trust. Furthermore, we discuss the future impact of cutting-edge technologies like quantum computing and federated learning on the evolution of ensemble techniques. The future of ensemble deep learning and machine learning is set to be shaped by the proliferation of big data, advancements in computational hardware, and the need for real-time, scalable solutions. This paper provides an extensive review of the current state of ensemble learning, identifies significant challenges, and suggests future research directions to fully harness the potential of these techniques in addressing real-world problems.
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