Abstract
This paper investigates problems related to quality assessment in the case of multi-label automatic classification of data, using k-Nearest Neighbor classifier. Various methods of assigning classes, as well as measures of assessing the quality of classification results are proposed and investigated both theoretically and in practical tests. In our experiments, audio data representing short music excerpts of various emotional contents were parameterized and then used for training and testing. Class labels represented emotions assigned to a given audio excerpt. The experiments show how various measures influence quality assessment of automatic classification of multi-label data.
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Wieczorkowska, A., Synak, P. (2006). Quality Assessment of k-NN Multi-label Classification for Music Data. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_45
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DOI: https://doi.org/10.1007/11875604_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
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