document: On demand classification of data streams Current models of the classification problem do not effectively handle bursts of particular classes coming in at different times . In fact , the current model of the classification problem simply concentrates on methods for one-pass classification modeling of very large data sets . Our model for data stream classification views the data stream classification problem from the point of view of a dynamic approach in which simultaneous training and testing streams are used for dynamic classification of data sets . This model reflects real life situations effectively , since it is desirable to classify test streams in real time over an evolving training and test stream . The aim here is to create a classification system in which the training model can adapt quickly to the changes of the underlying data stream . In order to achieve this goal , we propose an on-demand classification process which can dynamically select the appropriate window of past training data to build the classifier . The empirical results indicate that the system maintains a high classification accuracy in an evolving data stream , while providing an efficient solution to the classification task .
doc_bio_tags: O O B O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O