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146
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ICIC
2005
Springer
15 years 9 months ago
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced da...
Hui Han, Wenyuan Wang, Binghuan Mao
129
Voted
VISUALIZATION
2000
IEEE
15 years 8 months ago
Constructing material interfaces from data sets with volume-fraction information
We present a new algorithm for material boundary interface reconstruction from data sets containing volume fractions. We transform the reconstruction problem to a problem that ana...
Kathleen S. Bonnell, Kenneth I. Joy, Bernd Hamann,...
107
Voted
HAIS
2009
Springer
15 years 7 months ago
Beyond Homemade Artificial Data Sets
One of the most important challenges in supervised learning is how to evaluate the quality of the models evolved by different machine learning techniques. Up to now, we have relied...
Núria Macià, Albert Orriols-Puig, Es...
125
Voted
FLAIRS
2007
15 years 6 months ago
A Distance-Based Over-Sampling Method for Learning from Imbalanced Data Sets
Many real-world domains present the problem of imbalanced data sets, where examples of one classes significantly outnumber examples of other classes. This makes learning difficu...
Jorge de la Calleja, Olac Fuentes
138
Voted
CPC
2006
116views more  CPC 2006»
15 years 3 months ago
Finding Large Independent Sets in Polynomial Expected Time
We consider instances of the maximum independent set problem that are constructed according to the following semirandom model. Let Gn,p be a random graph, and let S be a set consis...
Amin Coja-Oghlan