Results 241 to 250 of about 141,868 (281)
Some of the next articles are maybe not open access.
IEEE Transactions on Knowledge and Data Engineering, 2009
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data
Haibo He
exaly +2 more sources
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data
Haibo He
exaly +2 more sources
No Free Lunch in imbalanced learning
Knowledge-Based Systems, 2021Abstract The No Free Lunch (NFL) theorems have sparked intense debate since their publication, from theoretical and practical perspectives. However, to this date, no discussion is provided concerning its impact in the established field of imbalanced domain learning (IDL), known for its challenges regarding learning and evaluation processes.
Nuno Moniz, Hugo Monteiro
openaire +1 more source
Imbalanced Label Distribution Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023Label distribution covers a certain number of labels, representing the degree to which each label describes an instance. The learning process on the instances labeled by label distributions is called Label Distribution Learning (LDL). Although LDL has been applied successfully to many practical applications, one problem with existing LDL methods is ...
Xingyu Zhao 0002 +4 more
openaire +1 more source
Learning in imbalanced relational data
2008 19th International Conference on Pattern Recognition, 2008Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes.
Amal Saleh Ghanem +2 more
openaire +1 more source
Learning Deep Landmarks for Imbalanced Classification
IEEE Transactions on Neural Networks and Learning Systems, 2020We introduce a deep imbalanced learning framework called learning DEep Landmarks in laTent spAce (DELTA). Our work is inspired by the shallow imbalanced learning approaches to rebalance imbalanced samples before feeding them to train a discriminative classifier.
Feng Bao, Yue Deng, Youyong Kong
exaly +3 more sources
Imbalanced Learning in Massive Phishing Datasets
2020 IEEE 6th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2020Phishing is one of the major threats facing internet users in today’s work. Such attacks continue costing billions of dollars to companies around the words thus requiring more efficient detection techniques to curb the danger. This paper proposes a big data friendly implementation of Multiclass Imbalance Learning in Ensembles through Selective Sampling
Ali Azari +4 more
openaire +1 more source
Boosting weighted ELM for imbalanced learning
Neurocomputing, 2014Extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) is a powerful machine learning technique, and has been attracting attentions for its fast learning speed and good generalization performance. Recently, a weighted ELM is proposed to deal with data with imbalanced class distribution. The key essence of weighted ELM
Kuan Li +4 more
openaire +2 more sources
2013
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learningImbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data.
openaire +1 more source
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learningImbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data.
openaire +1 more source
A learning strategy for highly imbalanced classification
Proceedings of the Third International Conference on Internet Multimedia Computing and Service, 2011This paper describes a new learning strategy on the problem of classification on overlapped and imbalanced training set. We devise an adaptive scheme for minority generating; with data cleaning of majority, new clusters are drawn to increasingly focus on the combination of new minority samples.
Tong Liu 0005 +2 more
openaire +1 more source
Ensemble Learning with Resampling for Imbalanced Data
2021Imbalanced class distribution is an issue that appears in various applications. In this paper, we undertake a comprehensive study of the effects of sampling on the performance of bootstrap aggregating in the context of imbalanced data. Concretely, we carry out a comparison of sampling methods applied to single and ensemble classifiers.
Firuz Kamalov +2 more
openaire +1 more source