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ECAI
2008
Springer
13 years 10 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo
SAC
2005
ACM
14 years 2 months ago
Learning decision trees from dynamic data streams
: This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental a...
João Gama, Pedro Medas, Pedro Pereira Rodri...
TJS
2010
182views more  TJS 2010»
13 years 7 months ago
A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Yasser Yasami, Saadat Pour Mozaffari
ICDE
2000
IEEE
130views Database» more  ICDE 2000»
14 years 10 months ago
CMP: A Fast Decision Tree Classifier Using Multivariate Predictions
Most decision tree classifiers are designed to keep class histograms for single attributes, and to select a particular attribute for the next split using said histograms. In this ...
Haixun Wang, Carlo Zaniolo
ECML
2005
Springer
14 years 2 months ago
Simple Test Strategies for Cost-Sensitive Decision Trees
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that ...
Shengli Sheng, Charles X. Ling, Qiang Yang