Results 31 to 40 of about 224,004 (328)
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets [PDF]
, 2016 Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections.
Bettinger, Franck +6 more
core +2 more sources
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
Distribution-sensitive learning for imbalanced datasets [PDF]
, 2013 Many real-world face and gesture datasets are by nature imbalanced across classes. Conventional statistical learning models (e.g., SVM, HMM, CRY), however, are sensitive to imbalanced datasets.
Davis, Randall +2 more
core +1 more source
Multi-class pattern classification in imbalanced data [PDF]
, 2010 The majority of multi-class pattern classification techniques are proposed for learning from balanced datasets. However, in several real-world domains, the datasets have imbalanced data distribution, where some classes of data may have few training ...
Ghanem, Amal S. +2 more
core +1 more source
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
doaj +1 more source
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
core +1 more source
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.
core +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
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
doaj +1 more source
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
core +2 more sources