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» Experimental perspectives on learning from imbalanced data
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EDM
2010
145views Data Mining» more  EDM 2010»
13 years 9 months ago
Mining Rare Association Rules from e-Learning Data
Rare association rules are those that only appear infrequently even though they are highly associated with very specific data. In consequence, these rules can be very appropriate f...
Cristóbal Romero, José Raúl R...
TNN
2010
127views Management» more  TNN 2010»
13 years 2 months ago
RAMOBoost: ranked minority oversampling in boosting
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imba...
Sheng Chen, Haibo He, Edwardo A. Garcia
KELSI
2004
Springer
14 years 1 months ago
Improving Rule Induction Precision for Automated Annotation by Balancing Skewed Data Sets
There is an overwhelming increase in submissions to genomic databases, posing a problem for database maintenance, especially regarding annotation of fields left blank during submi...
Gustavo E. A. P. A. Batista, Maria Carolina Monard...
ECML
2004
Springer
14 years 1 months ago
Applying Support Vector Machines to Imbalanced Datasets
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to...
Rehan Akbani, Stephen Kwek, Nathalie Japkowicz
ECIS
2000
13 years 9 months ago
Software Process Management: An Organizational Learning Perspective
We draw from the quality management and organizational learning literatures to develop a descriptive model of software process management. These literature streams suggest that the...
Thiagarajan Ravichandran, Arun Rai