首页|West Ukrainian National University Researchers Report Research in Machine Learni ng (OLTW-TEC: online learning with sliding windows for text classifier ensembles )
West Ukrainian National University Researchers Report Research in Machine Learni ng (OLTW-TEC: online learning with sliding windows for text classifier ensembles )
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
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from Ternopi l, Ukraine, by NewsRx correspondents, research stated, “In the digital age, rapi d dissemination of information has elevated the challenge of distinguishing betw een authentic news and disinformation. This challenge is particularly acute in r egions experiencing geopolitical tensions, where information plays a pivotal rol e in shaping public perception and policy.” Our news correspondents obtained a quote from the research from West Ukrainian N ational University: “The prevalence of disinformation in the Ukrainian-language information space, intensified by the hybrid war with russia, necessitates the d evelopment of sophisticated tools for its detection and mitigation. Our study in troduces the ‘Online Learning with Sliding Windows for Text Classifier Ensembles ’ (OLTWTEC) method, designed to address this urgent need. This research aims to develop and validate an advanced machine learning method capable of dynamically adapting to evolving disinformation tactics. The focus is on creating a highly accurate, flexible, and efficient system for detecting disinformation in Ukraini an-language texts. The OLTW-TEC method leverages an ensemble of classifiers comb ined with a sliding window technique to continuously update the model with the m ost recent data, enhancing its adaptability and accuracy over time. A unique dat aset comprising both authentic and fake news items was used to evaluate the meth od’s performance. Advanced metrics, including precision, recall, and F1- score, f acilitated a comprehensive analysis of its effectiveness. The OLTW-TEC method de monstrated exceptional performance, achieving a classification accuracy of 93% . The integration of the sliding window technique with a classifier ensemble sig nificantly contributed to the system’s ability to accurately identify disinforma tion, making it a robust tool in the ongoing battle against fake news in the Ukr ainian context. The application of the OLTW-TEC method highlights its potential as a versatile and effective solution for disinformation detection. Its adaptabi lity to the specifics of the Ukrainian language and the dynamic nature of inform ation warfare offers valuable insights into the development of similar tools for other languages and regions.”
West Ukrainian National UniversityTern opilUkraineEuropeCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
