Mining Battlefield Information Using Ensemble Classifiers

To help handle battlefield information superiority to decision superiority (i.e. to rapidly arrive at better decisions than adversaries can respond to), many scientific, technical and technological challenges must be addressed. The most critical of those are information fusion and management at different levels, communication. This paper decribes battlefield information as data streams and mining it using ensemble classifiers, and focusing on handling noisy and concept drift datas. Our theoretical and empirical study shows that our framework is superior and more robust to averaging ensemble for noisy battlefield information data streams.