Results 11 to 20 of about 90,025 (264)
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
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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
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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
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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
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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
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Evaluating Misclassifications in Imbalanced Data [PDF]
, 2006 Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable.
William Elazmeh +2 more
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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
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A method for increasing accuracy of credit imbalanced data [PDF]
فصلنامه بورس اوراق بهادارThe main goal of this research is to provide a method that can be used to increase the accuracy of credit imbalance data. Financial fraud is a fundamental problem that affects both the financial sector and life and plays an important role in affecting ...
Arash GhorbanniaDelavar +1 more
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Oversampling Techniques for Imbalanced Data in Regression
Expert Systems with Applications, 2023 Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (
Samir Brahim Belhaouari +4 more
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Bicriteria Oversampling for Imbalanced Data Classification
Procedia Computer Science, 2022 The paper proposes bicriteria oversampling strategy for mining imbalanced data. We use two specialized criteria for oversampling -classification potential and distance from the borderline between minority and majority instances. The potential is to be maximized and the distance minimized.
Joanna Jędrzejowicz +1 more
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