Results 181 to 190 of about 39,918 (213)
Some of the next articles are maybe not open access.
Balancing techniques for imbalanced datasets
Proceedings of the 8th International Research Congress REDU, 2022Luis Cedeño-Valarezo +3 more
openaire +1 more source
Discrimination aware classification for imbalanced datasets
Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute.
Goce Ristanoski, Wei Liu, James Bailey
openaire +1 more source
A Robust Classifier for Imbalanced Datasets
2014Imbalanced dataset classification is a challenging problem, since many classifiers are sensitive to class distribution so that the classifiers’ prediction has bias towards majority class. Hellinger Distance has been proven that it is skew-insensitive and the decision trees that employ Hellinger Distance as a splitting criterion have shown better ...
Sori Kang, Kotagiri Ramamohanarao
openaire +1 more source
Mapping Forests Using an Imbalanced Dataset
Journal of The Institution of Engineers (India): Series B, 2022Keerti Kulkarni, P. A. Vijaya
openaire +1 more source
Simulating Complexity Measures on Imbalanced Datasets
2020Classification tasks using imbalanced datasets are not challenging on their own. Classification models perform poorly on the minority class when the datasets present other difficulties, such as class overlap and complex decision border. Data complexity measures can identify such difficulties, better dealing with imbalanced datasets.
Victor H. Barella +2 more
openaire +1 more source
Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
Assessing Imbalanced Datasets in Binary Classifiers
2023Pooja Singh, Rajeev Kumar
openaire +1 more source
Overlapping Classes in Imbalanced Datasets
Big data has become easily available, but there is a need to improve the usefulness of these data, especially when we have an imbalanced dataset and overlapping data points in two or more classes. Machine-learning algorithms have improved in recent years, and many algorithms have been introduced that tackle the issues in data that su er from imbalancedopenaire +1 more source
A study on classifying imbalanced datasets
2014 First International Conference on Networks & Soft Computing (ICNSC2014), 2014T. Jaya Lakshmi, Ch. Siva Rama Prasad
openaire +1 more source