OASIS: Online adaptive ensembles for drift adaptation on evolving IoT data streams

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
T. Anithakumari, Sanket Mishra
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引用次数: 0

Abstract

In this work, our proposed OASIS framework utilizes adaptive ensembles to accommodate IoT data drift. In this work, we introduce an innovative sliding window approach using periodograms, engineered to efficiently feed models with data input. Six distinct online learners, alongside three drift adaptation algorithms: EDDM, HDDM-A and ADWIN have been tested using various feature selection methods, such as particle swarm optimization (PSO), dragonfly optimization (DA), grey wolf optimization (GWO), genetic algorithm (GA), and whale optimization algorithm (WOA), which have been carried out to validate the efficacy of the OASIS framework. We introduce a weighted probability approach derived from multiclass outcomes to ascertain the most suitable learners for leverage bagging or voting ensemble application. This is followed by an optimal scoring mechanism to determine the best training set based on accuracy and execution time criteria. The selection of models is guided by a probability-based algorithm coupled with a scoring system. Furthermore, we benchmark three state-of-the-art drift adaptation frameworks to evaluate their performance relative to our proposed framework. Evaluations in the context of EDGE-IIoT demonstrated outstanding accuracies of 98.98% in binary scenarios and 99.92% in multiclass scenarios, with the IoTID20 datasets achieving notable accuracies of 99.94% in binary and 100% in multiclass scenarios, thus surpassing previous methodologies. The framework undergoes extensive experiments with two recent multiclass datasets, namely the Aalto and RT-IoT 2022 datasets, in which OASIS achieved 99.99% accuracy on the Aalto dataset and 96.52% on the RT-IoT 2022 dataset. Additionally, we compare our framework with various concept drift datasets and leading drift ensemble frameworks for performance comparison.
OASIS:用于不断发展的物联网数据流漂移适应的在线自适应集成
在这项工作中,我们提出的OASIS框架利用自适应集成来适应物联网数据漂移。在这项工作中,我们引入了一种使用周期图的创新滑动窗口方法,旨在有效地为模型提供数据输入。采用粒子群优化(PSO)、蜻蜓优化(DA)、灰狼优化(GWO)、遗传算法(GA)和鲸鱼优化算法(WOA)等多种特征选择方法,对六种不同的在线学习器以及三种漂移适应算法(EDDM、HDDM-A和ADWIN)进行了测试,以验证OASIS框架的有效性。我们引入了一种从多类结果衍生的加权概率方法,以确定最适合杠杆bagging或投票集成应用的学习器。接下来是一个最优评分机制,以根据准确性和执行时间标准确定最佳训练集。模型的选择由基于概率的算法与评分系统相结合来指导。此外,我们对三个最先进的漂移适应框架进行了基准测试,以评估它们相对于我们提出的框架的性能。在EDGE-IIoT背景下的评估显示,在二进制场景下的准确率为98.98%,在多类场景下的准确率为99.92%,其中IoTID20数据集在二进制场景下的准确率为99.94%,在多类场景下的准确率为100%,从而超越了之前的方法。该框架在两个最新的多类数据集(即Aalto和RT-IoT 2022数据集)上进行了广泛的实验,其中OASIS在Aalto数据集上的准确率达到99.99%,在RT-IoT 2022数据集上的准确率达到96.52%。此外,我们将我们的框架与各种概念漂移数据集和领先的漂移集成框架进行了性能比较。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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