Abstract
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.
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Lesinski, W., Jastrzebska, A. (2015). Optical Music Recognition: Standard and Cost-Sensitive Learning with Imbalanced Data. In: Saeed, K., Homenda, W. (eds) Computer Information Systems and Industrial Management. CISIM 2015. Lecture Notes in Computer Science(), vol 9339. Springer, Cham. https://doi.org/10.1007/978-3-319-24369-6_51
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DOI: https://doi.org/10.1007/978-3-319-24369-6_51
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