Aljaz Osojnik

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Multi-label classification (MLC) tasks are encountered more and more frequently in machine learning applications. While MLC methods exist for the classical batch setting, only a few methods are available for streaming setting. In this paper, we propose a new methodology for MLC via multi-target regression in a streaming setting. Moreover, we develop a(More)
We address the task of modeling dynamical systems in discrete time using regression trees, model trees and option trees for on-line regression. Some challenges that modeling dynamical systems pose to data mining approaches are described: these motivate the use of methods for mining data streams. The algorithm FIMT-DD for mining data streams with regression(More)
Methods that address the task of multi-target regression on data streams are relatively weakly represented in the current literature. We present several different approaches to learning trees and ensembles of trees for multi-target regression based on the Hoeffding bound. First, we introduce a local method, which learns multiple single-target trees to(More)
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