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» Learning to Identify Unexpected Instances in the Test Set
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SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
13 years 8 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...
FLAIRS
2008
13 years 9 months ago
Selecting Minority Examples from Misclassified Data for Over-Sampling
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method sel...
Jorge de la Calleja, Olac Fuentes, Jesús Go...
EC
2008
146views ECommerce» more  EC 2008»
13 years 7 months ago
Automated Discovery of Local Search Heuristics for Satisfiability Testing
The development of successful metaheuristic algorithms such as local search for a difficult problems such as satisfiability testing (SAT) is a challenging task. We investigate an ...
Alex S. Fukunaga
ACMSE
2006
ACM
14 years 1 months ago
A SAT-based solver for Q-ALL SAT
Although the satisfiability problem (SAT) is NP-complete, state-of-the-art solvers for SAT can solve instances that are considered to be very hard. Emerging applications demand t...
Ben Browning, Anja Remshagen
CIKM
2005
Springer
14 years 27 days ago
Information retrieval and machine learning for probabilistic schema matching
Schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distribute...
Henrik Nottelmann, Umberto Straccia