%0 Conference Proceedings
%T Optical Music Recognition: Standard and Cost-Sensitive Learning with Imbalanced Data
%+ Białystok University of Technology
%+ Warsaw University of Technology [Warsaw]
%A Lesinski, Wojciech
%A Jastrzebska, Agnieszka
%Z Part 9: Music Information Processing Workshop
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th Computer Information Systems and Industrial Management (CISIM)
%C Warsaw, Poland
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer
%3 Computer Information Systems and Industrial Management
%V LNCS-9339
%P 601-612
%8 2015-09-24
%D 2015
%R 10.1007/978-3-319-24369-6_51
%K Cost-sensitive learning
%K SVM
%K Random forest
%K Feature selection
%K Optical music recognition
%K Imbalanced data
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X The article is focused on a particular aspect of classification, namely the issue of class imbalance. Imbalanced data adversely affects the recognition ability and requires proper classifier’s construction. In this work we present a case of music notation as an example of imbalanced data. Three classification algorithms - random forest, standard SVM and cost-sensitive SVM are described and tested. Feature selection based on random forest feature importance was used. Also, feature dimension reduction using PCA was studied.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01444503/document
%2 https://inria.hal.science/hal-01444503/file/978-3-319-24369-6_51_Chapter.pdf
%L hal-01444503
%U https://inria.hal.science/hal-01444503
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-9339