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Available methods that estimate feature importances on data streams have thus far focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression, iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the robustness of iSOUP-SymRF and the feature rankings it produces in terms of the methods\u2019 parameters: the size of the ensemble and the number of selected features. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings.<\/jats:p>","DOI":"10.1007\/978-3-031-45275-8_4","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T06:01:56Z","timestamp":1696658516000},"page":"48-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["iSOUP-SymRF: Symbolic Feature Ranking with\u00a0Random Forests in\u00a0Online Multi-target Regression"],"prefix":"10.1007","author":[{"given":"Alja\u017e","family":"Osojnik","sequence":"first","affiliation":[]},{"given":"Pan\u010de","family":"Panov","sequence":"additional","affiliation":[]},{"given":"Sa\u0161o","family":"D\u017eeroski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"4_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-642-03915-7_22","volume-title":"Advances in Intelligent Data Analysis VIII","author":"A Bifet","year":"2009","unstructured":"Bifet, A., Gavald\u00e0, R.: Adaptive learning from evolving data streams. 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