Scalable Nonparametric Supervised Learning for Streaming and Massive Data: Applications in Healthcare Monitoring and Credit Risk

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mohamed Chaouch;Omama M. Al-Hamed
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引用次数: 0

Abstract

This paper introduces novel nonparametric supervised learning techniques for classifying massive datasets, addressing key limitations of existing methods in Big and Streaming Data framework. We propose an offline kernel-based classifier enhanced by Batch Principal Component Analysis (PCA) for dimensionality reduction to mitigate the “curse of dimensionality”. Additionally, an online classifier is developed for streaming data, combining online PCA with a kernel-based recursive classifier using a stochastic approximation algorithm. Application to fetal well-being monitoring demonstrates that the online classifier achieves a competitive median misclassification rate (11.92%), comparable to the offline classifier (11.54%) and Random Forest (11.31%), while requiring only 1/15th of the offline classifier’s computation time. Receiver Operating Characteristic (ROC) analysis shows superior Area Under the Curve (AUC) for the offline classifier but at a significant computational cost. A second study on larger database of credit scoring confirms these findings, showing that the online classifier achieves an F1-score of 96.40% and an accuracy of 93.08%, closely matching the performance of neural networks (96.46%, 93.22%) and boosting (96.51%, 93.31%). Notably, the online classifier accomplishes this with a CPU time of only 0.87 seconds per classification - over 600 times faster than neural networks - demonstrating its effectiveness for high-frequency, real-time financial decision-making.
流和海量数据的可扩展非参数监督学习:在医疗监控和信用风险中的应用
本文介绍了一种新的非参数监督学习技术,用于对海量数据集进行分类,解决了大数据和流数据框架中现有方法的主要局限性。我们提出了一种基于离线核的分类器,通过批处理主成分分析(PCA)进行降维,以减轻“维数诅咒”。此外,针对流数据开发了一种在线分类器,将在线主成分分析与基于核的递归分类器结合使用随机逼近算法。在胎儿健康监测中的应用表明,在线分类器实现了具有竞争力的中位数误分类率(11.92%),与离线分类器(11.54%)和随机森林(11.31%)相当,而所需的计算时间仅为离线分类器的1/15。接受者工作特征(ROC)分析显示离线分类器的曲线下面积(AUC)优越,但计算成本很高。第二项对更大的信用评分数据库的研究证实了这些发现,表明在线分类器的f1得分为96.40%,准确率为93.08%,与神经网络(96.46%,93.22%)和boosting(96.51%, 93.31%)的性能非常接近。值得注意的是,在线分类器每次分类的CPU时间仅为0.87秒,比神经网络快600多倍,这证明了它在高频、实时金融决策方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
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