Abstract
The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. The paper contains a comparative study of results of musical symbols classification using known algorithms: k-nearest neighbors, k-means, Mahalanobis minimal distance, and decision trees. Authors aim at addressing the problem of imbalanced pattern recognition. First, we theoretically analyze difficulties entailed in the classification of music notation symbols. Second, in the enclosed case study we investigate the fitness of named single classifiers on real data. Conducted experiments are based on own implementations of named algorithms with all necessary image processing tasks. Results are highly satisfying.
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Acknowledgments
The research is supported by the National Science Center, grant No 2011/01/B/ST6/06478, decision no DEC-2011/01/B/ST6/06478.
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Jastrzebska, A., Lesinski, W. (2016). Optical Music Recognition as the Case of Imbalanced Pattern Recognition: A Study of Single Classifiers. In: Skulimowski, A., Kacprzyk, J. (eds) Knowledge, Information and Creativity Support Systems: Recent Trends, Advances and Solutions. Advances in Intelligent Systems and Computing, vol 364. Springer, Cham. https://doi.org/10.1007/978-3-319-19090-7_37
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DOI: https://doi.org/10.1007/978-3-319-19090-7_37
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