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New construction of Ensemble Classifiers for imbalanced datasets
2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2010Learning in the presence of data imbalances presents a great challenge to machine learning. Imbalanced data sets represent a significant problem because the corresponding classifier has a tendency to ignore samples which have smaller representation in the training sets.
Yun Zhai, Da Ruan 0001, Nan Ma, Bing An
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A comparison for handling imbalanced datasets
2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014In various real case, imbalanced datasets problems are inevitable, such as in metal detecting security or diagnosis of disease. With the limitations of existing learning algorithms when faced with imbalanced datasets, the prediction error is caused by the dominance of the majority against the minority class. Various techniques have been made to address
Arif Syaripudin, Masayu Leylia Khodra
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A Practical Anonymization Approach for Imbalanced Datasets
IT Professional, 2022Abdul Majeed 0001, Seong Oun Hwang
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To improve classification of imbalanced datasets
2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017The task of accurately predicting the target class for each case in the data is called classification of data in data mining. Classification of balanced data set is fairly simple and easy to perform but it becomes difficult when the data is not balanced.
Pratyusha Shukla, Kiran Bhowmick
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Learning Curve Estimation with Large Imbalanced Datasets
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Datasets for machine learning are constantly increasing in size, along with computational requirements for processing the data. A useful exercise for machine learning experiments is to approximate model performance as dataset size increases. This can inform application building and data collection efforts as well as improve computational efficiency by ...
Aaron N. Richter, Taghi M. Khoshgoftaar
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Passive OS Identification in Imbalanced Dataset
2023 International Conference on Electrical, Computer and Energy Technologies (ICECET), 2023Jingzhi Li, Ziling Wei, Shuhui Chen
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Optimisation and Evaluation of Random Forests for Imbalanced Datasets
2006This paper deals with an optimization of Random Forests which aims at: adapting the concept of forest for learning imbalanced data as well as taking into account user's wishes as far as recall and precision rates are concerned. We propose to adapt Random Forest on two levels.
Julien Thomas +2 more
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A review of methods for imbalanced multi-label classification
Pattern Recognition, 2021Adane Nega Tarekegn +2 more
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