Optical Music Recognition: Standard and Cost-Sensitive Learning with Imbalanced Data

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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Wojciech Lesinski, Agnieszka Jastrzebska. Optical Music Recognition: Standard and Cost-Sensitive Learning with Imbalanced Data. 14th Computer Information Systems and Industrial Management (CISIM), Sep 2015, Warsaw, Poland. pp.601-612, ⟨10.1007/978-3-319-24369-6_51⟩. ⟨hal-01444503⟩

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