{"date_updated":"2023-03-15T13:49:38Z","user_id":"15514","date_created":"2019-11-25T08:35:44Z","language":[{"iso":"eng"}],"title":"Linear Classification of Badly Conditioned Data. ","type":"conference","citation":{"ufg":"<b>Dörksen, Helene/Lohweg, Volker (2018)</b>: Linear Classification of Badly Conditioned Data. , in: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, Torino, Italy.","bjps":"<b>Dörksen H and Lohweg V</b> (2018) Linear Classification of Badly Conditioned Data. . <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy.","van":"Dörksen H, Lohweg V. Linear Classification of Badly Conditioned Data. . In: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Torino, Italy; 2018.","apa":"Dörksen, H., &#38; Lohweg, V. (2018). Linear Classification of Badly Conditioned Data. . In <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy. <a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">https://doi.org/10.1109/ETFA.2018.8502485</a>","short":"H. Dörksen, V. Lohweg, in: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.","chicago":"Dörksen, Helene, and Volker Lohweg. “Linear Classification of Badly Conditioned Data. .” In <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy, 2018. <a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">https://doi.org/10.1109/ETFA.2018.8502485</a>.","havard":"H. Dörksen, V. Lohweg, Linear Classification of Badly Conditioned Data. , in: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: Linear Classification of Badly Conditioned Data. . In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy, 2018","ama":"Dörksen H, Lohweg V. Linear Classification of Badly Conditioned Data. . In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy; 2018. doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">10.1109/ETFA.2018.8502485</a>","ieee":"H. Dörksen and V. Lohweg, “Linear Classification of Badly Conditioned Data. ,” in <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>,  Turin, Italy , 2018.","chicago-de":"Dörksen, Helene und Volker Lohweg. 2018. Linear Classification of Badly Conditioned Data. . In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy. doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502485,\">10.1109/ETFA.2018.8502485,</a> .","mla":"Dörksen, Helene, and Volker Lohweg. “Linear Classification of Badly Conditioned Data. .” <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, 2018, doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">10.1109/ETFA.2018.8502485</a>."},"doi":"10.1109/ETFA.2018.8502485","place":"Torino, Italy","department":[{"_id":"DEP5023"}],"status":"public","main_file_link":[{"url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8502485","open_access":"1"}],"author":[{"last_name":"Dörksen","first_name":"Helene","id":"46416","full_name":"Dörksen, Helene"},{"full_name":"Lohweg, Volker","orcid":"0000-0002-3325-7887","id":"1804","first_name":"Volker","last_name":"Lohweg"}],"keyword":["Task analysis","Software","Linear discriminant analysis","Dimensionality reduction","Mathematical model","Covariance matrices","Measurement"],"publication":"23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","conference":{"location":" Turin, Italy ","name":" IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 2018","end_date":"2018-09-07","start_date":"2018-09-04"},"abstract":[{"text":"We present a method for the fast and robust linear classification of badly conditioned data. In our considerations, badly conditioned data are such data which are numerically difficult to handle. Due to, e.g. a large number of features or a large number of objects representing classes as well as noise, outliers or incompleteness, the common software computation of the discriminating linear combination of features between classes fails or is extremely time consuming. The theoretical foundations of our approach are based on the single feature ranking, which allows fast calculation of the approximative initial classification boundary. For the increasing of classification accuracy of this boundary, the refinement is performed in the lower dimensional space. Our approach is tested on several datasets from UCI Reposi-tiory. Experimental results indicate high classification accuracy of the approach. For the modern real industrial applications such a method is especially suitable in the Cyber-Physical-System environments and provides a part of the workflow for the automated classifier design","lang":"eng"}],"oa":"1","year":2018,"_id":"2005"}