Adaptive Fast XGBoost for Multiclass Classification - Baldo, Fabiano; Grando, Julia; Yamada Correa, Yuji; Amorim Policarpo, Deividy (Journal of Information and Data Management, 2023)
The popularization of sensoring and connectivity technologies like 5G and IoT are boosting the generation of data streams. Such kinds of data are one of the last frontiers of data mining applications. However, data streams are massive and unbounded sequences of non-stationary data objects that are continuously generated at rapid rates. To deal with these challenges, the learning algorithms should analyze the data just once and update their classifiers to handle the concept drifts. The literature presents some algorithms to deal with the classification of multiclass data streams. However, most of them have high processing time. Therefore, this work proposes a XGBoost-based classifier called AFXGB-MC to fast classify non-stationary data streams with multiple classes. We compared it with the six state-of-the-art algorithms for multiclass classification found in the literature. The results pointed out that AFXGB-MC presents similar accuracy performance, but with faster processing time, being twice faster than the second fastest algorithm from the literature, and having fast drift recovery time.