Results 21 to 30 of about 103,585 (306)
A Method for Analyzing the Performance Impact of Imbalanced Binary Data on Machine Learning Models
Axioms, 2022 Machine learning models may not be able to effectively learn and predict from imbalanced data in the fields of machine learning and data mining. This study proposed a method for analyzing the performance impact of imbalanced binary data on machine ...
Ming Zheng +5 more
doaj +1 more source
Imbalanced Data Classification Method Based on LSSASMOTE
IEEE Access, 2023 Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning. In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper ...
Zhi Wang, Qicheng Liu
doaj +1 more source
Classification Algorithm for Structured Imbalanced Data Based on Convolutional Neural Network [PDF]
Jisuanji gongcheng, 2023 Convolutional Neural Network(CNN) are widely used in image processing, object tracking, natural language, and other fields because of their efficient feature extraction capabilities and their use of fewer parameters.To address the problem in which ...
XU Hong, JIAO Guie, ZHANG Wenjun, CHEN Yimin
doaj +1 more source
Interpretable ML for Imbalanced Data
CoRR, 2022 Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships between classes may be highly skewed and unclear.
Damien A. Dablain +4 more
openaire +2 more sources
Partial Resampling of Imbalanced Data
CoRR, 2022 Imbalanced data is a frequently encountered problem in machine learning. Despite a vast amount of literature on sampling techniques for imbalanced data, there is a limited number of studies that address the issue of the optimal sampling ratio. In this paper, we attempt to fill the gap in the literature by conducting a large scale study of the effects ...
Firuz Kamalov +2 more
openaire +2 more sources
An Imbalanced Data Rule Learner [PDF]
, 2005 Imbalanced data learning has recently begun to receive much attention from research and industrial communities as traditional machine learners no longer give satisfactory results. Solutions to the problem generally attempt to adapt standard learners to the imbalanced data setting.
Canh Hao Nguyen, Tu Bao Ho
openaire +1 more source
Mine Classification With Imbalanced Data [PDF]
IEEE Geoscience and Remote Sensing Letters, 2009 In many remote-sensing classification problems, the number of targets (e.g., mines) present is very small compared with the number of clutter objects. Traditional classification approaches usually ignore this class imbalance, causing performance to suffer accordingly.
David P. Williams +2 more
openaire +1 more source
Multicriteria Classifier Ensemble Learning for Imbalanced Data
IEEE Access, 2022 One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that ...
Weronika Wegier +2 more
doaj +1 more source
Boosting methods for multi‑class imbalanced data classification
, 2021 Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets.
Abdi, Y (via Mendeley Data)
core +2 more sources
Data Augmentation for Imbalanced Regression
CoRR, 2023 paper accepted at the AISTATS 2023 conference, to be published in PMLR (Proceedings of Machine Learning Research)
Samuel Stocksieker +2 more
openaire +3 more sources