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ICDM
2008
IEEE
107views Data Mining» more  ICDM 2008»
15 years 10 months ago
Graph-Based Iterative Hybrid Feature Selection
When the number of labeled examples is limited, traditional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample problem. To ...
ErHeng Zhong, Sihong Xie, Wei Fan, Jiangtao Ren, J...
ICPR
2004
IEEE
16 years 5 months ago
Efficient Calculation of the Complete Optimal Classification Set
Feature and structure selection is an important part of many classification problems. In previous papers, an approach called basis pursuit classification has been proposed which p...
Martin Brown, Nicholas Costen, Shigeru Akamatsu
AUSAI
2009
Springer
15 years 8 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...
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BMCBI
2006
110views more  BMCBI 2006»
15 years 4 months ago
Bias in error estimation when using cross-validation for model selection
Background: Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have proposed methods for optimizing classifiers...
Sudhir Varma, Richard Simon
APNOMS
2008
Springer
15 years 6 months ago
Empirical Analysis of Application-Level Traffic Classification Using Supervised Machine Learning
Accurate application traffic classification and identification are important for network monitoring and analysis. The accuracy of traditional Internet application traffic classific...
Byungchul Park, Young J. Won, Mi-Jung Choi, Myung-...