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 Bayesian, 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 advant...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han
• Keywords: Classification Accuracy, Data Mining, KDD 2003, Mining Data Streams, Weighted Ensemble Classifiers