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KAIS
2010
144views more  KAIS 2010»
13 years 4 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
ICIC
2005
Springer
13 years 11 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
AUSAI
2009
Springer
13 years 9 months ago
Ensemble Approach for the Classification of Imbalanced Data
Ensembles are often capable of greater prediction accuracy than any of their individual members. As a consequence of the diversity between individual base-learners, an ensemble wil...
Vladimir Nikulin, Geoffrey J. McLachlan, Shu-Kay N...
FUIN
2002
107views more  FUIN 2002»
13 years 5 months ago
Towards an Ontology of Approximate Reason
Abstract. This article introduces structural aspects in an ontology of approximate reason. The basic assumption in this ontology is that approximate reason is a capability of an ag...
James F. Peters, Andrzej Skowron, Jaroslaw Stepani...
KES
2005
Springer
13 years 11 months ago
OntoExtractor: A Fuzzy-Based Approach in Clustering Semi-structured Data Sources and Metadata Generation
This paper describes a theoretical approach on data mining, information classifying and a global overview of our OntoExtractor application, concerning the analysis of incoming data...
Zhan Cui, Ernesto Damiani, Marcello Leida, Marco V...