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» A supervised learning approach for imbalanced data sets
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NIPS
1994
13 years 10 months ago
Efficient Methods for Dealing with Missing Data in Supervised Learning
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
Volker Tresp, Ralph Neuneier, Subutai Ahmad
BMCBI
2008
136views more  BMCBI 2008»
13 years 9 months ago
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
TKDE
2008
148views more  TKDE 2008»
13 years 9 months ago
Semisupervised Clustering with Metric Learning using Relative Comparisons
Semisupervised clustering algorithms partition a given data set using limited supervision from the user. The success of these algorithms depends on the type of supervision and also...
Nimit Kumar, Krishna Kummamuru
KCAP
2005
ACM
14 years 2 months ago
AutoFeed: an unsupervised learning system for generating webfeeds
The AutoFeed system automatically extracts data from semistructured web sites. Previously, researchers have developed two types of supervised learning approaches for extracting we...
Bora Gazen, Steven Minton
LREC
2008
110views Education» more  LREC 2008»
13 years 10 months ago
Cost-Sensitive Learning in Answer Extraction
One problem of data-driven answer extraction in open-domain factoid question answering is that the class distribution of labeled training data is fairly imbalanced. This imbalance...
Michael Wiegand, Jochen L. Leidner, Dietrich Klako...