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INFORMATICALT
2008
196views more  INFORMATICALT 2008»
13 years 4 months ago
An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams
Abstract. Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data blo...
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
ICTAI
2007
IEEE
13 years 11 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 classifiers by using Genetic Programmi...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...
CIS
2004
Springer
13 years 10 months ago
Knowledge Maintenance on Data Streams with Concept Drifting
Concept drifting in data streams often occurs unpredictably at any time. Currently many classification mining algorithms deal with this problem by using an incremental learning ap...
Juggapong Natwichai, Xue Li
ICDM
2010
IEEE
199views Data Mining» more  ICDM 2010»
13 years 2 months ago
Addressing Concept-Evolution in Concept-Drifting Data Streams
Abstract--The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two s...
Mohammad M. Masud, Qing Chen, Latifur Khan, Charu ...
JMLR
2010
182views more  JMLR 2010»
12 years 11 months ago
Decision Tree for Dynamic and Uncertain Data Streams
Current research on data stream classification mainly focuses on certain data, in which precise and definite value is usually assumed. However, data with uncertainty is quite natu...
Chunquan Liang, Yang Zhang, Qun Song