Detecting concept drift in fully distributed environments

Applying sophisticated machine learning techniques on fully distributed data is increasingly important in many applications like distributed recommender systems or spam filters. In this type of networked environment the data model can change dynamically over time (concept drift). Identifying when concept drift occurred is a key for several drift handling techniques and important in numerous scenarios. However drift handling approaches exist, no efficient solution for detecting the drift is known in very large scale networks. Here, we propose an approach that can detect the concept drift in large scale and fully distributed networks. In our approach, the learning is performed by applying online learners that take random walks in the network while updating themselves using the samples available at the nodes. The drift detection is based on an adaptive mechanism which uses the historical performances of the models. Through empirical evaluations we demonstrate that our approach handles the drifting concept while additionally detects the occurrence of the concept drift with high accuracy.