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» Combining Two Data Mining Methods for System Identification
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ICDM
2003
IEEE
102views Data Mining» more  ICDM 2003»
14 years 1 months ago
Bootstrapping Rule Induction
Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to ...
Lemuel R. Waitman, Douglas H. Fisher, Paul H. King
WWW
2006
ACM
14 years 8 months ago
Probabilistic models for discovering e-communities
The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis...
Ding Zhou, Eren Manavoglu, Jia Li, C. Lee Giles, H...
WPES
2004
ACM
14 years 1 months ago
Assessing global disclosure risk in masked microdata
In this paper, we introduce a general framework for microdata and three disclosure risk measures (minimal, maximal and weighted). We classify the attributes from a given microdata...
Traian Marius Truta, Farshad Fotouhi, Daniel C. Ba...
EXPERT
1998
83views more  EXPERT 1998»
13 years 7 months ago
Data-Driven Constructive Induction
Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one—for the “best” representation space, and two—for the “be...
Eric Bloedorn, Ryszard S. Michalski
ESANN
2008
13 years 9 months ago
A method for robust variable selection with significance assessment
Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need ...
Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Ales...