Fiche du document
24 septembre 2015
- handle: 10670/1.xxr9l4
- hal: hal-01444503
- doi: 10.1007/978-3-319-24369-6_51
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-24369-6_51
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Cost-sensitive learning SVM Random forest Feature selection Optical music recognition Imbalanced dataSujets proches
Serious music Musical compositions Art music Musical works Classical music Western music (Western countries) Art music, Western Western art musicCiter ce document
Wojciech Lesinski et al., « Optical Music Recognition: Standard and Cost-Sensitive Learning with Imbalanced Data », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-319-24369-6_51
Métriques
Partage / Export
Résumé
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.