Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Iosifidis, Vasileios"'
Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, asses
Externí odkaz:
http://arxiv.org/abs/2211.04812
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classe
Externí odkaz:
http://arxiv.org/abs/2209.08309
Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race. One reason for this behavior is the encoded societal biases in the training data (e.g., females are underrepresented), which is aggravated in
Externí odkaz:
http://arxiv.org/abs/2201.01148
As decision-making increasingly relies on Machine Learning (ML) and (big) data, the issue of fairness in data-driven Artificial Intelligence (AI) systems is receiving increasing attention from both research and industry. A large variety of fairness-a
Externí odkaz:
http://arxiv.org/abs/2110.00530
Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc. The underlying data distribution might evolve over time callin
Externí odkaz:
http://arxiv.org/abs/2108.06231
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced. The class-imbalance problem is more severe for the often underrepresen
Externí odkaz:
http://arxiv.org/abs/2104.13312
Autor:
Huang, Xin, Zhang, Wenbin, Tang, Xuejiao, Zhang, Mingli, Surbiryala, Jayachander, Iosifidis, Vasileios, Liu, Zhen, Zhang, Ji
Recent studies in big data analytics and natural language processing develop automatic techniques in analyzing sentiment in the social media information. In addition, the growing user base of social media and the high volume of posts also provide val
Externí odkaz:
http://arxiv.org/abs/2103.14804
Autor:
Tang, Xuejiao, Qiu, Jiong, Chen, Ruijun, Zhang, Wenbin, Iosifidis, Vasileios, Liu, Zhen, Meng, Wei, Zhang, Mingli, Zhang, Ji
An ideal safe workplace is described as a place where staffs fulfill responsibilities in a well-organized order, potential hazardous events are being monitored in real-time, as well as the number of accidents and relevant damages are minimized. Howev
Externí odkaz:
http://arxiv.org/abs/2012.03190
Autor:
Tang, Xuejiao, Zhang, Liuhua, Zhang, Wenbin, Huang, Xin, Iosifidis, Vasileios, Liu, Zhen, Zhang, Mingli, Messina, Enza, Zhang, Ji
Breast cancer is the second leading cause of cancer-related death after lung cancer in women. Early detection of breast cancer in X-ray mammography is believed to have effectively reduced the mortality rate. However, a relatively high false positive
Externí odkaz:
http://arxiv.org/abs/2012.03151
Autor:
Hu, Tongxin, Iosifidis, Vasileios, Liao, Wentong, Zhang, Hang, YingYang, Michael, Ntoutsi, Eirini, Rosenhahn, Bodo
In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressi
Externí odkaz:
http://arxiv.org/abs/2004.02173