%0 Conference Proceedings
%T Empirical Assessment of Performance Measures for Preprocessing Moments in Imbalanced Data Classification Problem
%+ Białystok University of Technology
%A Szeszko, Paweł
%A Topczewska, Magdalena
%Z Part 3: Images, Visualization, Classification
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Vilnius, Lithuania
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-9842
%P 183-194
%8 2016-09-14
%D 2016
%R 10.1007/978-3-319-45378-1_17
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X The article concerns the problem of imbalanced data classification, when classes, into which elements belong, are not equally represented. In the classification model building process cross-validation technique is one of the most popular to assess the efficacy of a classifier. While over-sampling methods are used to create new objects to obtain the balance between the number of objects in classes, inappropriate usage of the preprocessing moment has a direct impact on the achieved results. In most cases they are overestimated. To present and assess this phenomenon in this paper three preprocessing techniques (SMOTE, Safe-level SMOTE, SPIDER) and their modifications are used to make new elements of data sets to balance cardinalities of classes, and two classification methods (SVM, C4.5) are compared. k-folds cross-validation technique ($$k=10$$) considering two moments of preprocessing approaches is performed. The measures as precision, recall, F-measure and area under the ROC curve (AUC) are calculated and compared.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01637457/document
%2 https://inria.hal.science/hal-01637457/file/419526_1_En_17_Chapter.pdf
%L hal-01637457
%U https://inria.hal.science/hal-01637457
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-9842