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127
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RSFDGRC
2005
Springer
117views Data Mining» more  RSFDGRC 2005»
15 years 9 months ago
Dependency Bagging
In this paper, a new variant of Bagging named DepenBag is proposed. This algorithm obtains bootstrap samples at first. Then, it employs a causal discoverer to induce from each sam...
Yuan Jiang, Jinjiang Ling, Gang Li, Honghua Dai, Z...
167
Voted
PKDD
2004
Springer
168views Data Mining» more  PKDD 2004»
15 years 9 months ago
Combining Winnow and Orthogonal Sparse Bigrams for Incremental Spam Filtering
Spam filtering is a text categorization task that has attracted significant attention due to the increasingly huge amounts of junk email on the Internet. While current best-pract...
Christian Siefkes, Fidelis Assis, Shalendra Chhabr...
142
Voted
KDD
2008
ACM
174views Data Mining» more  KDD 2008»
16 years 4 months ago
Automatic identification of quasi-experimental designs for discovering causal knowledge
Researchers in the social and behavioral sciences routinely rely on quasi-experimental designs to discover knowledge from large databases. Quasi-experimental designs (QEDs) exploi...
David D. Jensen, Andrew S. Fast, Brian J. Taylor, ...
152
Voted
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 8 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
126
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UAI
2003
15 years 5 months ago
Learning Module Networks
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...