Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Some issues on detecting emotions in music
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Recognition of instrument timbres in real polytimbral audio recordings
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
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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.