Al-Dawsari Amal, Al-Turaiki Isra, Kurdi Heba
Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.
Sensors (Basel). 2022 Mar 13;22(6):2223. doi: 10.3390/s22062223.
Internet of Things (IoT) environments produce large amounts of data that are challenging to analyze. The most challenging aspect is reducing the quantity of consumed resources and time required to retrain a machine learning model as new data records arrive. Therefore, for big data analytics in IoT environments where datasets are highly dynamic, evolving over time, it is highly advised to adopt an online (also called incremental) machine learning model that can analyze incoming data instantaneously, rather than an offline model (also called static), that should be retrained on the entire dataset as new records arrive. The main contribution of this paper is to introduce the Incremental Ant-Miner (IAM), a machine learning algorithm for online prediction based on one of the most well-established machine learning algorithms, Ant-Miner. IAM classifier tackles the challenge of reducing the time and space overheads associated with the classic offline classifiers, when used for online prediction. IAM can be exploited in managing dynamic environments to ensure timely and space-efficient prediction, achieving high accuracy, precision, recall, and F-measure scores. To show its effectiveness, the proposed IAM was run on six different datasets from different domains, namely horse colic, credit cards, flags, ionosphere, and two breast cancer datasets. The performance of the proposed model was compared to ten state-of-the-art classifiers: naive Bayes, logistic regression, multilayer perceptron, support vector machine, K*, adaptive boosting (AdaBoost), bagging, Projective Adaptive Resonance Theory (PART), decision tree (C4.5), and random forest. The experimental results illustrate the superiority of IAM as it outperformed all the benchmarks in nearly all performance measures. Additionally, IAM only needs to be rerun on the new data increment rather than the entire big dataset on the arrival of new data records, which makes IAM better in time- and resource-saving. These results demonstrate the strong potential and efficiency of the IAM classifier for big data analytics in various areas.
物联网（IoT）环境会产生大量难以分析的数据。最具挑战性的方面是，随着新数据记录的到来，减少重新训练机器学习模型所需的资源消耗和时间。因此，对于物联网环境中的大数据分析，由于数据集具有高度动态性且随时间不断演变，强烈建议采用能够即时分析传入数据的在线（也称为增量式）机器学习模型，而不是离线模型（也称为静态模型），离线模型在新记录到来时需要在整个数据集上重新训练。本文的主要贡献是引入了增量蚁群算法（IAM），这是一种基于最成熟的机器学习算法之一蚁群算法的在线预测机器学习算法。IAM分类器解决了在用于在线预测时与经典离线分类器相关的时间和空间开销问题。IAM可用于管理动态环境，以确保及时且节省空间的预测，实现高精度、精准率、召回率和F1值分数。为了证明其有效性，在来自不同领域的六个不同数据集上运行了所提出的IAM，即马绞痛、信用卡、旗帜、电离层以及两个乳腺癌数据集。将所提出模型的性能与十个最先进的分类器进行了比较：朴素贝叶斯、逻辑回归、多层感知器、支持向量机、K*、自适应增强（AdaBoost）、装袋法、投影自适应共振理论（PART）、决策树（C4.5）和随机森林。实验结果表明了IAM的优越性，因为它在几乎所有性能指标上都优于所有基准。此外，在新数据记录到来时，IAM只需要在新数据增量上重新运行，而不是在整个大数据集上重新运行，这使得IAM在节省时间和资源方面表现更优。这些结果证明了IAM分类器在各个领域进行大数据分析方面具有强大的潜力和效率。
