Intelligent IoT Traffic Classification Using Novel Search Strategy for Fast-Based-Correlation Feature Selection in Industrial Environments

• Published in: IEEE internet of things journal Vol. 5; no. 3; pp. 1616 - 1624
• Main Authors: Egea, Santiago, Rego Mañez, Albert, Carro, Belén, Sánchez-Esguevillas, Antonio, Lloret, Jaime
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.06.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Classification algorithms; Correlation; Correlation-based methods; Damage detection; emergency detection; Feature extraction; Feature selection; filter methods; Filtering algorithms; Industries; industry; Information filters; Internet of Things; Internet of Things (IoT); Machine learning; Machine learning algorithms; Model accuracy; Multimedia; multimedia traffic
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2017.2787959

Internet of Things (IoT) can be combined with machine learning in order to provide intelligent applications to the network nodes. Furthermore, IoT expands these advantages and technologies to the industry. In this paper, we propose a modification of one of the most popular algorithms for feature selection, fast-based-correlation feature (FCBF). The key idea is to split the feature space in fragments with the same size. By introducing this division, we can improve the correlation and, therefore, the machine learning applications that are operating on each node. This kind of IoT applications for industry allows us to separate and prioritize the sensor data from the multimedia-related traffic. With this separation, the sensors are able to detect efficiently emergency situations and avoid both material and human damage. The results show the performance of the three FCBF-based algorithms for different problems and different classifiers, confirming the improvements achieved by our approach in terms of model accuracy and execution time.