首页|New Machine Learning Research from King Saud University Outlined (The Impact of the Weighted Features on the Accuracy of X-Platform's User Credibility Detection Using Supervised Machine Learning)
New Machine Learning Research from King Saud University Outlined (The Impact of the Weighted Features on the Accuracy of X-Platform's User Credibility Detection Using Supervised Machine Learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
Investigators publish new report on artificial intelligence. According to news reporting originating from Riyadh, Saudi Arabia, by NewsRx correspondents, research stated, “Social media represent a vital actor in our lives, often serving as a primary source of information, surpassing traditional sources. Among these platforms, the X-Platform, which used to be called Twitter, has emerged as a leading space for the exchange of opinions and emotions.” Funders for this research include Deputyship For Research And Innovation, Ministry of Education, Saudi Arabia. Our news correspondents obtained a quote from the research from King Saud University: “In this study, we introduced a supervised machine learning system designed to detect user credibility in this influential platform. User credibility detection depends largely on the features of the users on the platform. Feature weighting plays a pivotal role in identifying the significance of each feature in a dataset. It can indicate irrelevant features, which can lead to better performance in classification problems. This study aims to highlight the impact of weighted features on the accuracy of X-Platform User Credibility Detection (XUCD) using supervised machine learning methods, such as Principal Component Analysis (PCA) and correlationcoefficient algorithms, and tree-based methods, such as (ExtraTressClarifier) to extract new weighted features in the dataset and then use them to train our model to discover their impact on the accuracy of user credibility detection issues. As a result, we measured the effectiveness of different feature-weighting methods on different dataset categories to determine which obtained the best detection accuracy.”
King Saud UniversityRiyadhSaudi ArabiaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
