摘要
随着大数据时代的到来,自动化数据挖掘在各个领域的应用日益广泛。通过对深度学习、强化学习等人工智能技术在数据挖掘中的应用进行研究,文章提出了一种基于改进型遗传算法的自适应参数优化方法。实验表明,该方法在处理高维数据时,相比传统算法可将计算时间减少35%,准确率提升18%。同时,针对噪声数据的处理能力得到显著提升,在实际业务场景中表现出较强的鲁棒性和适应性。通过对金融、医疗等领域的实际数据集进行验证,证实了该方法在自动化特征工程和模型选择方面的优越性。
With the advent of the big data era,the application of automated data mining in various fields has become increasingly widespread.The study investigates the application of artificial intelligence technologies,such as deep learning and reinforcement learning,in data mining and proposes an adaptive parameter optimization method based on an improved genetic algorithm.Experimental results demonstrate that this method reduces computation time by 35%and improves accuracy by 18%compared to traditional algorithms when processing high-dimensional data.Additionally,the method significantly enhances the ability to handle noisy data,exhibiting strong robustness and adaptability in real-world business scenarios.Validation using real-world datasets from fields such as finance and healthcare confirms the superiority of this method in automated feature engineering and model selection.
作者
窦玉姣
DOU Yujiao(Guangdong Mechanical Technician College,Guangzhou Guangdong 510450,China)
出处
《信息与电脑》
2025年第10期16-18,共3页
Information & Computer
关键词
人工智能
数据挖掘
算法优化
遗传算法
自适应参数
artificial intelligence
data mining
algorithm optimization
genetic algorithm
adaptive parameters
作者简介
窦玉姣,女,本科,讲师。研究方向:计算机科学与技术、计算机广告。
