Novel Class Detection in Concept-Drifting Data Stream Mining Employing Decision Tree

In this paper, we propose a new approach for detecting novel class in data stream mining using decision tree classifier that can determine whether an unseen or new instance belongs to a novel class. Most existing data mining classifiers can not detect and classify the novel class instances in real-time data stream mining problems like weather conditions, economical changes, astronomical, and intrusion detection etc, untill the classification models are trained with the labeled instances of the novel class. Arrival of a novel class in concept-drift occurs in data stream mining when new data introduce the new concept classes or remove the old ones. The proposed approach for incremental learning of concept drift considers mining, where the streaming data distributions change over time. It build a decision tree model from training dataset, which continuously updates so that the tree represents the most recent concept in data stream. The experiments on real benchmark data evaluate the efficiency of the proposed approach in both detecting the novel class and classification accuracy with comparisons of traditional data mining classifiers.