Random Forests of Very Fast Decision Trees on GPU for Mining Evolving Big Data Streams
Diego Marrón, Albert Bifet, De Francisci Morales Gianmarco · Frontiers in artificial intelligence and applications · 2014
Random Forest is a classical ensemble method used to improve the performance of single tree classifiers. It is able to obtain superior performance by increasing the diversity of the single classifiers. However, in the more challenging context of evolving data streams, the classifier has also to be adaptive and work under very strict constraints of space and time. Furthermore, the computational load of using a large number of classifiers can make its application extremely expensive.