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ESANN
2006
15 years 6 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...
ICDM
2010
IEEE
228views Data Mining» more  ICDM 2010»
15 years 2 months ago
Multi-label Feature Selection for Graph Classification
Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug acti...
Xiangnan Kong, Philip S. Yu
MICRO
2002
IEEE
164views Hardware» more  MICRO 2002»
15 years 10 months ago
A quantitative framework for automated pre-execution thread selection
Pre-execution attacks cache misses for which conventional address-prediction driven prefetching is ineffective. In pre-execution, copies of cache miss computations are isolated fr...
Amir Roth, Gurindar S. Sohi
157
Voted
IJCV
1998
238views more  IJCV 1998»
15 years 4 months ago
Feature Detection with Automatic Scale Selection
The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the not...
Tony Lindeberg
TCBB
2010
176views more  TCBB 2010»
15 years 3 months ago
Feature Selection for Gene Expression Using Model-Based Entropy
—Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best...
Shenghuo Zhu, Dingding Wang, Kai Yu, Tao Li, Yihon...