Results 51 to 60 of about 103,585 (306)
Severely imbalanced Big Data challenges: investigating data sampling approaches
Journal of Big Data, 2019 Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class.
Tawfiq Hasanin +3 more
doaj +1 more source
Extending Bagging for Imbalanced Data [PDF]
, 2013 Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside ...
Jerzy Blaszczynski +2 more
openaire +1 more source
Oversampling Algorithm Oriented to Subdivision of Minority Class in Imbalanced Data Set [PDF]
Jisuanji gongcheng, 2017 The distributions of the minority class samples in the imbalanced data set are discrepant.Traditional oversampling algorithms do not dispose this discrepancy.In order to handle this discrepancy,this paper proposes an oversampling algorithm oriented to ...
GU Ping,YANG Yang
doaj +1 more source
, 2020 Binary datasets are considered imbalanced when one of their two classes has less than 40% of the total number of the data instances (i.e., minority class).
Ulukok, Mehtap Kose +3 more
core +1 more source
Enhancing classification performance of multi-class imbalanced data using the OAA-DB algorithm
, 2012 In data classification, the problem of imbalanced class distribution has attracted many attentions. Most efforts have used to investigate the problem mainly for binary classification.
Wong, K.W. +3 more
core +1 more source
A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems
, 2011 This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier ...
Xia Hong +8 more
core +1 more source
A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
Abstract and Applied Analysis, 2013 In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers.
Yong Zhang, Dapeng Wang
doaj +1 more source
Probability density function estimation based over-sampling for imbalanced two-class problems
, 2012 A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic ...
Xia Hong +10 more
core +1 more source
Reacting Imbalanced Data via Ensemble Learning Techniques [PDF]
IJCI International Journal of Computers and InformationIn machine learning, dealing with imbalanced datasets remains a significant challenge. Class imbalance arises when the distribution of instances across classes is uneven, which can occur in both binary and multiclass problems with varying imbalance ...
Fatma Kindeel +2 more
doaj +1 more source
Data-Centric Optimization Approach for Small, Imbalanced Datasets
Journal of Information and Organizational Sciences, 2023 Data-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning.
Vladislav Tanov
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