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» Ensembles of Multi-Objective Decision Trees
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CIS
2004
Springer
15 years 2 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
IJSI
2008
156views more  IJSI 2008»
14 years 9 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker
KDD
2009
ACM
146views Data Mining» more  KDD 2009»
15 years 4 months ago
Mining in a mobile environment
Distributed PRocessing in Mobile Environments (DPRiME) is a framework for processing large data sets across an ad-hoc network. Developed to address the shortcomings of Google’s ...
Sean McRoskey, James Notwell, Nitesh V. Chawla, Ch...
EVOW
2008
Springer
14 years 11 months ago
A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification
Classifier fusion strategies have shown great potential to enhance the performance of pattern recognition systems. There is an agreement among researchers in classifier combination...
Amin Assareh, Mohammad Hassan Moradi, L. Gwenn Vol...
KDD
2004
ACM
117views Data Mining» more  KDD 2004»
15 years 9 months ago
Systematic data selection to mine concept-drifting data streams
One major problem of existing methods to mine data streams is that it makes ad hoc choices to combine most recent data with some amount of old data to search the new hypothesis. T...
Wei Fan