Results 21 to 30 of about 90,025 (264)
Predicting Financial Bubbles with Imbalanced Data [PDF]
ITM Web of ConferencesThis paper explores the prediction of financial bubbles within the S&P 500 using a machine learning framework as well as econometric bubble detection tests.
Bogoslavskiy Alexei
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Box drawings for learning with imbalanced data [PDF]
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems.
Siong Thye Goh, Cynthia Rudin
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A New Big Data Model Using Distributed Cluster-Based Resampling for Class-Imbalance Problem
Applied Computer Systems, 2019 The class imbalance problem, one of the common data irregularities, causes the development of under-represented models. To resolve this issue, the present study proposes a new cluster-based MapReduce design, entitled Distributed Cluster-based Resampling ...
Terzi Duygu Sinanc, Sagiroglu Seref
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Spectral clustering with imbalanced data [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $ε$-graphs. This is because the objectives such as Ratio-Cut
Jing Qian, Venkatesh Saligrama
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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
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SN Applied Sciences, 2021 Considering the complexities and challenges in the classification of multiclass and imbalanced fault conditions, this study explores the systematic combination of unsupervised and supervised learning by hybridising clustering (CLUST) and optimised multi ...
Albert Buabeng +3 more
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Improved PSO_AdaBoost Ensemble Algorithm for Imbalanced Data
Sensors, 2019 The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework, and it can get good classification results on general datasets.
Kewen Li +4 more
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A Novel Imbalanced Ensemble Learning in Software Defect Predication
IEEE Access, 2021 With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the
Jianming Zheng +4 more
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Imbalanced Data Classification Algorithm Based on CSD-ELM [PDF]
Jisuanji gongcheng, 2019 The Extreme Learning Machine(ELM) based on cost-sensitive learning has its advantages in dealing with imbalanced data classification problems.However,it fails to consider the distribution characteristics of samples in different classes and the importance
WANG Dafei, XIE Wujie, DONG Wenhan
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IEEE Access, 2022 The imbalanced datasets and their classification has pulled in as a hot research topic over the years. It is used in different fields, for example, security, finance, health, and many others.
Abeer S. Desuky +4 more
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