Multi-label classification via multi-target regression on data streams

Abstract

Multi-label classification is becoming more and more critical in data mining applications. Many efficient methods exist in the classical batch setting, however, in the streaming setting, comparatively few methods exist. In this paper, we propose a new methodology for multilabel classification via multi-target regression in a streaming setting and develop a streaming multi-target regressor iSOUP-Tree, which uses this approach. We experimentally evaluated two variants of the iSOUP-Tree algorithm, and determined that the use of regression trees is advisable over the use model trees. Furthermore, we compared our results to the state-of-the-art and found that the iSOUP-Tree method is comparable to the other streaming multi-label learners. This is a motivation for the potential use of iSOUP-Tree in an ensemble setting as a base learner.