Sciweavers

Share
14 search results - page 1 / 3
» Suppressing model overfitting in mining concept-drifting dat...
Sort
View
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
10 years 11 months ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
SDM
2007
SIAM
140views Data Mining» more  SDM 2007»
9 years 12 months ago
A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions
In recent years, there have been some interesting studies on predictive modeling in data streams. However, most such studies assume relatively balanced and stable data streams but...
Jing Gao, Wei Fan, Jiawei Han, Philip S. Yu
ICTAI
2007
IEEE
10 years 4 months ago
An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams
An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classi´Čüers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
10 years 11 months ago
Real-time ranking with concept drift using expert advice
In many practical applications, one is interested in generating a ranked list of items using information mined from continuous streams of data. For example, in the context of comp...
Hila Becker, Marta Arias
ASC
2008
9 years 10 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
books