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This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms. <\/jats:p>","DOI":"10.1177\/1473871617713337","type":"journal-article","created":{"date-parts":[[2017,6,27]],"date-time":"2017-06-27T10:37:35Z","timestamp":1498559855000},"page":"282-305","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":43,"title":["Projections as visual aids for classification system design"],"prefix":"10.1177","volume":"17","author":[{"given":"Paulo E","family":"Rauber","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands"},{"name":"University of Campinas, Campinas, Brazil"}]},{"given":"Alexandre X","family":"Falc\u00e3o","sequence":"additional","affiliation":[{"name":"University of Campinas, Campinas, Brazil"}]},{"given":"Alexandru C","family":"Telea","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands"}]}],"member":"179","published-online":{"date-parts":[[2017,6,27]]},"reference":[{"key":"bibr1-1473871617713337","volume-title":"Machine learning: a probabilistic perspective","author":"Murphy KP.","year":"2012"},{"key":"bibr2-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-007-9039-3"},{"key":"bibr3-1473871617713337","first-page":"1157","volume":"3","author":"Guyon I","year":"2003","journal-title":"J Mach Learn Res"},{"key":"bibr4-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.66"},{"key":"bibr5-1473871617713337","volume-title":"Proceedings of the advances in neural information processing systems","author":"Krizhevsky A"},{"key":"bibr6-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1561\/2200000006"},{"key":"bibr7-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"bibr8-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1996.8.7.1341"},{"key":"bibr9-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1145\/2347736.2347755"},{"key":"bibr10-1473871617713337","volume-title":"Proceedings of the fifth workshop on beyond time and errors: novel evaluation methods for visualization (BELIV)","author":"Brehmer M"},{"key":"bibr11-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12366"},{"key":"bibr12-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2004.11.042"},{"key":"bibr13-1473871617713337","first-page":"2579","volume":"9","author":"Van der Maaten L","year":"2008","journal-title":"J Mach Learn Res"},{"key":"bibr14-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2007.70443"},{"key":"bibr15-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2011.220"},{"key":"bibr16-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp215"},{"key":"bibr17-1473871617713337","first-page":"1145","volume":"14","author":"Chen L","year":"2013","journal-title":"J Mach Learn Res"},{"key":"bibr18-1473871617713337","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2640960"},{"key":"bibr19-1473871617713337","unstructured":"Heinrich J, Weiskopf D. 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