Abstract
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.
Highlights
An online machine learning model is one of the most effective approaches in machine learning [1,2], where the model is continuously adapted based on arriving data [3–7]
The highest values are bolded for each performance measure
The highest recall value was 97.5%, achieved by naïve Bayes, and the highest F-measure was 95.5%, achieved by random forest. These results demonstrate that the Incremental Ant-Miner (IAM) algorithms outperformed all the state-of-the-art algorithms regarding accuracy, precision, and F-measure
Summary
An online ( called incremental) machine learning model is one of the most effective approaches in machine learning [1,2], where the model is continuously adapted based on arriving data [3–7]. Online learning is becoming increasingly essential in the big data analytics domain where datasets evolve over time, such as precision agriculture [10,11], flood prediction [12,13], and business activities [6,14,15]. Due to their success, a growing body of literature has been proposed for developing incremental machine learning models. In contrast to hard (conventional) computing, refers to approximate solutions based on artificial intelligence (AI) that are tolerant of imprecision, uncertainty and partial truth. It provides cost-effective solutions to complex real-life problems in different fields, e.g., [16–18]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
