Magnetic Force Classifier: A Novel Method for Big Data Classification

IEEE Access

Mohammad Ali Abbadi¹  Ahmad S. Tarawneh²  Hasan N. Ali³  Ahmad B. Hassanat³  Ghada Awad Altarawneh⁴  Mansoor Alghamdi⁵  Malek Alrashidi⁶ 

[1] Computer Science Department, Applied College, University of Tabuk, Tabuk, Saudi Arabia;Department of Algorithms and Their Applications, E&x00F6;Faculty of Information Technology, Mutah University, Karak, Jordan;nd University, Budapest, Hungary;s Lor&x00E1;tv&x00F6;

关键词: Artificial intelligence;    classification algorithms;    data mining;    supervised learning;    machine learning;   

DOI  :  10.1109/ACCESS.2022.3142888

来源: DOAJ

【 摘 要 】

There are a plethora of invented classifiers in Machine learning literature, however, there is no optimal classifier in terms of accuracy and time taken to build the trained model, especially with the tremendous development and growth of Big data. Hence, there is still room for improvement. In this paper, we propose a new classification method that is based on the well-known magnetic force. Based on the number of points belonging to a specific class/magnet, the proposed magnetic force (MF) classifier calculates the magnetic force at each discrete point in the feature space. Unknown examples are classified using the magnetic forces recorded in the trained model by various magnets/classes. When compared to existing classifiers, the proposed MF classifier achieves comparable classification accuracy, according to the experimental results utilizing 28 different datasets. More importantly, we found that the proposed MF classifier is significantly faster than all other classifiers tested, particularly when applied to Big datasets and hence could be a viable option for structured Big data classification with some optimization.

【 授权许可】

Unknown