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» Experimental perspectives on learning from imbalanced data
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WWW
2008
ACM
14 years 8 months ago
Floatcascade learning for fast imbalanced web mining
This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the "Matthew Effect". As web IC applicatio...
Xiaoxun Zhang, Xueying Wang, Honglei Guo, Zhili Gu...
DMIN
2007
226views Data Mining» more  DMIN 2007»
13 years 9 months ago
Generative Oversampling for Mining Imbalanced Datasets
— One way to handle data mining problems where class prior probabilities and/or misclassification costs between classes are highly unequal is to resample the data until a new, d...
Alexander Liu, Joydeep Ghosh, Cheryl Martin
SYNTHESE
2008
55views more  SYNTHESE 2008»
13 years 7 months ago
The semantics/pragmatics interface from an experimental perspective: the case of scalar implicature
In this paper I discuss some of the criteria that are widely used in the linguistic and philosophical literature to classify an aspect of meaning as either semantic or pragmatic. W...
Napoleon Katsos
SDM
2008
SIAM
177views Data Mining» more  SDM 2008»
13 years 9 months ago
Roughly Balanced Bagging for Imbalanced Data
Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distri...
Shohei Hido, Hisashi Kashima
KAIS
2010
144views more  KAIS 2010»
13 years 6 months ago
Boosting support vector machines for imbalanced data sets
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers t...
Benjamin X. Wang, Nathalie Japkowicz