TOMBoost: a topic modeling based boosting approach for learning with class imbalance
Classification of data with imbalanced characteristics is an essential research problem as
the data from most real-world applications follow non-uniform class proportions. Solutions to …
the data from most real-world applications follow non-uniform class proportions. Solutions to …
Soft computational techniques to identify cotton leaf damage
RF Caldeira, WE Santiago… - Australian Journal of Crop …, 2021 - search.informit.org
The principal objective of agriculture is the production of a high yield of healthy crops. This
yield may be improved by the automatic detection of diseases and the consequent reduction …
yield may be improved by the automatic detection of diseases and the consequent reduction …
A Novelty Adversarial Loss for Classifying Unbalanced Anomaly Images
W Huang, Q Zeng, P Li, A Jia, J Sona… - … Congress on Image …, 2022 - ieeexplore.ieee.org
The class imbalance problem has lately witnessed substantial progress attributed to the
powerful feature representation capability of deep convolutional neural networks (CNNs) …
powerful feature representation capability of deep convolutional neural networks (CNNs) …
The minimum ratio of preserving the dataset similarity in resampling: (1 − 1/e)
F Bulut - International Journal of Information Technology, 2020 - Springer
Pattern recognition, data mining and machine learning disciplines always work with a
predefined dataset to create a hypothesis for an artificial decision support system. A dataset …
predefined dataset to create a hypothesis for an artificial decision support system. A dataset …