document: Mining concept-drifting data streams using ensemble classifiers Recently , mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud protection , target marketing , network intrusion detection , etc. . Conventional knowledge discovery tools are facing two challenges , the overwhelming volume of the streaming data , and the concept drifts . In this paper , we propose a general framework for mining concept-drifting data streams using weighted ensemble classifiers . We train an ensemble of classification models , such as C4 .5 , RIPPER , naive Beyesian , etc. , from sequential chunks of the data stream . The classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving environment . Thus , the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classification . Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy , and the ensemble framework is effective for a variety of classification models .
doc_bio_tags: O O B I O O O O O O B I 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 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 I 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 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 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