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AUSAI
2009
Springer
15 years 6 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...
ESEM
2007
ACM
15 years 6 months ago
The Effects of Over and Under Sampling on Fault-prone Module Detection
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which ar...
Yasutaka Kamei, Akito Monden, Shinsuke Matsumoto, ...
AAAI
2007
15 years 4 months ago
Combining Multiple Heuristics Online
We present black-box techniques for learning how to interleave the execution of multiple heuristics in order to improve average-case performance. In our model, a user is given a s...
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm...
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FLAIRS
2007
15 years 4 months ago
Learning to Identify Global Bottlenecks in Constraint Satisfaction Search
Using information from failures to guide subsequent search is an important technique for solving combinatorial problems in domains such as boolean satisfiability (SAT) and constr...
Diarmuid Grimes, Richard J. Wallace
NIPS
2008
15 years 3 months ago
Human Active Learning
We investigate a topic at the interface of machine learning and cognitive science. Human active learning, where learners can actively query the world for information, is contraste...
Rui M. Castro, Charles Kalish, Robert Nowak, Ruich...