An Adaptive Ensemble Classifier for Mining Concept-Drifting Data Streams

Abstract

Traditional data mining techniques cannot be directly applied to the real-time data streaming environment. Existing mining classifiers therefore need to be updated frequently to adopt the changes in data streams. In this paper, we address this issue and propose an adaptive ensemble approach for classification and novel class detection in concept-drifting data streams. The proposed approach uses traditional mining classifiers and updates the ensemble model automatically so that it represents the most recent concepts in data streams. For novel class detection we consider the idea that data points belonging to the same class should be closer to each other and should be far apart from the data points belonging to other classes. If a data point is well separated from the existing data clusters, it is identified as a novel class instance. We tested the performance of this proposed stream classification model against existing mining algorithms using real benchmark datasets from UCI machine learning repository. The experimental results proved that our approach shows great flexibility and robustness in novel class detection in concept-drifting and outperforms traditional classification models in challenging real-life data stream applications.