caa a22 4500 606160302 CHVBK 20210128100629.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s00521-014-1780-0 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1780-0 Using Voronoi diagrams to improve classification performances when modeling imbalanced datasets [Elektronische Daten] [William Young II, Scott Nykl, Gary Weckman, David Chelberg] An over-sampling technique called V-synth is proposed and compared to borderline SMOTE (bSMOTE), a common methodology used to balance an imbalanced dataset for classification purposes. V-synth is a machine learning methodology that allows synthetic minority points to be generated based on the properties of a Voronoi diagram. A Voronoi diagram is a collection of geometric regions that encapsulate classifying points in such a way that any point within the region is closest to the encapsulated classifier than any other adjacent classifiers based on their distance from one another. Because of properties inherent to Voronoi diagrams, V-synth identifies exclusive regions of feature space where it is ideal to create synthetic minority samples. To test the generalization and application of V-synth, six databases from various problem domains were selected from the University of California Irvine's Machine Learning Repository. Though not always guaranteed due to the random nature of synthetic over-sampling, significant evidence is presented that supports the hypothesis that V-synth more consistently leads to the creation of more accurate and better-balanced classification models than bSMOTE when the classification complexity of a dataset is high. The Natural Computing Applications Forum, 2014 Data mining nationallicence Knowledge extraction nationallicence Data engineering nationallicence Numerical algorithms nationallicence Imbalanced datasets nationallicence Synthetic over-sampling nationallicence Young II William Department of Management, College of Business, Ohio University, 45701-2979, Athens, OH, USA aut Nykl Scott School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, 45701-2979, Athens, OH, USA aut Weckman Gary Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, 45701-2979, Athens, OH, USA aut Chelberg David School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, 45701-2979, Athens, OH, USA aut Neural Computing and Applications Springer London 26/5(2015-07-01), 1041-1054 0941-0643 26:5<1041 2015 26 521 https://doi.org/10.1007/s00521-014-1780-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1780-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Young II William Department of Management, College of Business, Ohio University, 45701-2979, Athens, OH, USA aut NATIONALLICENCE 700 1- Nykl Scott School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, 45701-2979, Athens, OH, USA aut NATIONALLICENCE 700 1- Weckman Gary Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, 45701-2979, Athens, OH, USA aut NATIONALLICENCE 700 1- Chelberg David School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, 45701-2979, Athens, OH, USA aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/5(2015-07-01), 1041-1054 0941-0643 26:5<1041 2015 26 521