Results 31 to 40 of about 4,590,312 (339)
Agronomy, 2023 The foundation of effectively predicting plant disease in the early stage using deep learning algorithms is ideal for addressing food insecurity, inevitably drawing researchers and agricultural specialists to contribute to its effectiveness.
Mike O. Ojo, Azlan Zahid
semanticscholar +1 more source
Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data
Journal of Big Data, 2019 The integrity of modern network communications is constantly being challenged by more sophisticated intrusion techniques. Attackers are consistently shifting to stealthier and more complex forms of attacks in an attempt to bypass known mitigation ...
Chad L. Calvert, Taghi M. Khoshgoftaar
doaj +1 more source
Multi-fairness Under Class-Imbalance
, 2022 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 underrepresented protected group (e.g. female, non-white, etc.) in the critical minority class.
Arjun Roy 0001 +2 more
openaire +2 more sources
International Journal of Financial Studies, 2023 The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class.
Patience Chew Yee Cheah +2 more
semanticscholar +1 more source
Stop Oversampling for Class Imbalance Learning: A Review
IEEE Access, 2022 For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand, is a concern. That is,
Ahmad S. Tarawneh +3 more
doaj +1 more source
Progressive Boosting for Class Imbalance
CoRR, 2017 Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using skewed data tend to recognize the majority class better than the minority class of interest.
Roghayeh Soleymani +2 more
openaire +2 more sources
Journal of Supercomputing, 2023 Network intrusion detection systems (NIDS) are the most common tool used to detect malicious attacks on a network. They help prevent the ever-increasing different attacks and provide better security for the network.
A. Abdelkhalek, M. Mashaly
semanticscholar +1 more source
Class Balanced Loss for Image Classification
IEEE Access, 2020 In the study of image classification, neural network learning relies heavily on datasets. Due to variability in the difficulty of collecting images in reality, datasets tend to have class imbalance problems, which undoubtedly increases the difficulty of ...
Lin Wang +4 more
doaj +1 more source
An Experimental Study of Class Imbalance in Federated Learning [PDF]
, 2021 Federated learning is a distributed machine learning paradigm that trains a global model for prediction based on several local models at clients while local data privacy is preserved. Class imbalance is believed to be one of the factors that degrades the
Wang, Shuo +3 more
core +1 more source
Addressing class imbalance in soil movement predictions [PDF]
Natural Hazards and Earth System SciencesLandslides threaten human life and infrastructure, resulting in fatalities and economic losses. Monitoring stations provide valuable data for predicting soil movement, which is crucial in mitigating this threat.
P. Kumar +3 more
doaj +1 more source