Change detection and adaptation in multi-target prediction on data streamsStevanoski, Bozhidar (Avtor) Džeroski, Sašo (Mentor) machine learningdata streamschange detection and adaptationmulti-target predictionmulti-target regressionAn essential characteristic of data streams is the possibility of occurrence of concept drift, i.e., change in the distribution of the data in the stream over time. The capability to detect and adapt to changes in data stream mining methods is thus a necessity. While methods for multi-target prediction on data streams have recently appeared, they have largely remained without such capability. In this thesis, we develop methods for change detection and adaptation in the context of incremental online learning of decision trees for multi-target regression. One of the approaches we propose is ensemble based, while the other uses the Page-Hinckley test. We extend both to also operate in the context of semi-supervised learning from partially labeled data. We perform an extensive evaluation of the proposed methods on real-world and artificial data streams and show their effectiveness, also on a case study from spacecraft operations.20212021-09-21 09:10:00Magistrsko delo/naloga131001sl