caa a22 4500 445871253 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110701xx s 000 0 eng 10.1007/s10618-010-0201-y doi (NATIONALLICENCE)springer-10.1007/s10618-010-0201-y Learning model trees from evolving data streams [Elektronische Daten] [Elena Ikonomovska, João Gama, Sašo Džeroski] The problem of real-time extraction of meaningful patterns from time-changing data streams is of increasing importance for the machine learning and data mining communities. Regression in time-changing data streams is a relatively unexplored topic, despite the apparent applications. This paper proposes an efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams. The algorithm is evaluated extensively in a variety of settings involving artificial and real data. To the best of our knowledge there is no other general purpose algorithm for incremental learning regression/model trees able to perform explicit change detection and informed adaptation. The algorithm performs online and in real-time, observes each example only once at the speed of arrival, and maintains at any-time a ready-to-use model tree. The tree leaves contain linear models induced online from the examples assigned to them, a process with low complexity. The algorithm has mechanisms for drift detection and model adaptation, which enable it to maintain accurate and updated regression models at any time. The drift detection mechanism exploits the structure of the tree in the process of local change detection. As a response to local drift, the algorithm is able to update the tree structure only locally. This approach improves the any-time performance and greatly reduces the costs of adaptation. The Author(s), 2010 Non-stationary data streams nationallicence Stream data mining nationallicence Regression trees nationallicence Model trees nationallicence Incremental algorithms nationallicence On-line learning nationallicence Concept drift nationallicence On-line change detection nationallicence Ikonomovska Elena Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia aut Gama João LIAAD/INESC, University of Porto, Rua de Ceuta, 118-6, 4050-190, Porto, Portugal aut Džeroski Sašo Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/1(2011-07-01), 128-168 1384-5810 23:1<128 2011 23 10618 https://doi.org/10.1007/s10618-010-0201-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0201-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ikonomovska Elena Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia aut NATIONALLICENCE 700 1- Gama João LIAAD/INESC, University of Porto, Rua de Ceuta, 118-6, 4050-190, Porto, Portugal aut NATIONALLICENCE 700 1- Džeroski Sašo Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 23/1(2011-07-01), 128-168 1384-5810 23:1<128 2011 23 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer