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ML
2006
ACM
163views Machine Learning» more  ML 2006»
14 years 9 months ago
Extremely randomized trees
Abstract This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute ...
Pierre Geurts, Damien Ernst, Louis Wehenkel
ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
14 years 7 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava
NECO
2006
157views more  NECO 2006»
14 years 9 months ago
Experiments with AdaBoost.RT, an Improved Boosting Scheme for Regression
The application of boosting technique to the regression problems has received relatively little attention in contrast to the research aimed at classification problems. This paper ...
Durga L. Shrestha, Dimitri P. Solomatine
ML
2000
ACM
185views Machine Learning» more  ML 2000»
14 years 9 months ago
A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms
Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the ca...
Tjen-Sien Lim, Wei-Yin Loh, Yu-Shan Shih
ESEM
2008
ACM
14 years 11 months ago
A hybrid faulty module prediction using association rule mining and logistic regression analysis
This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key m...
Yasutaka Kamei, Akito Monden, Shuuji Morisaki, Ken...