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Imbalanced Ensemble Classifier for learning from imbalanced business school data set
, 2018 Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets are biased towards one class, i.e., imbalanced in nature.
Chakraborty, Tanujit
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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
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Assessing Cultural and Ecological Variation in Ethnobiological Research: The Importance of Gender [PDF]
, 2005 Contending that a significant portion of current ethnobiological research continues to overlook cultural variation in traditional ecological knowledge (TEK) and practice, this paper explores the potential impacts of gender-imbalanced research on data ...
Pfeiffer, Jeanine M.
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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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Connection Science, 2019 Learning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the number of instances belonging to the other class ...
N. K. Sreeja
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MCMC for Imbalanced Categorical Data
, 2017 Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty.
Dunson, David B. +3 more
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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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Processing imbalanced medical data at the data level with assisted-reproduction data as an example
BioData MiningObjective Data imbalance is a pervasive issue in medical data mining, often leading to biased and unreliable predictive models. This study aims to address the urgent need for effective strategies to mitigate the impact of data imbalance on classification
Junliang Zhu +6 more
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MPSUBoost: A Modified Particle Stacking Undersampling Boosting Method
IEEE Access, 2022 Class imbalance problems are prevalent in the real world. In such cases, traditional supervised algorithms tend to have difficulty in recognizing minority data because the models are likely to maximize prediction accuracy by simply ignoring minority data.
Sang-Jin Kim, Dong-Joon Lim
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Deep Over-sampling Framework for Classifying Imbalanced Data
, 2017 Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured
B Krawczyk +15 more
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