Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Inductive learning in less than one sequential data scan
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Presently, inductive learning is still performed in a frustratingbatch process. The user has little interaction withthe system and no control over the final accuracy and trainingtime. If the accuracy of the produced model is too low,all the computing resources are misspent. In this paper, wepropose a progressive modeling framework. In progressivemodeling, the learning algorithm estimates online both theaccuracy of the final model and remaining training time. Ifthe estimated accuracy is far below expectation, the usercan terminate training prior to completion without wastingfurther resources. If the user chooses to complete the learningprocess, progressive modeling will compute a modelwith expected accuracy in expected time. We describe oneimplementation of progressive modeling using ensemble ofclassifiers.