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» An Exploratory Analysis of Multiple Multivariate Time Series
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ESANN
2006
14 years 11 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...
GRID
2007
Springer
15 years 1 months ago
Optimizing multiple queries on scientific datasets with partial replicas
We propose strategies to efficiently execute a query workload, which consists of multiple related queries submitted against a scientific dataset, on a distributed-memory system in...
Li Weng, Ümit V. Çatalyürek, Tahs...
ANOR
2010
135views more  ANOR 2010»
14 years 10 months ago
A framework of irregularity enlightenment for data pre-processing in data mining
Abstract Irregularities are widespread in large databases and often lead to erroneous conclusions with respect to data mining and statistical analysis. For example, considerable bi...
Siu-Tong Au, Rong Duan, Siamak G. Hesar, Wei Jiang
BMCBI
2007
215views more  BMCBI 2007»
14 years 10 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
JMS
2010
90views more  JMS 2010»
14 years 8 months ago
Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis
This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes ...
Kristien Van Loon, Fabián Güiza, Geert...