Abstract
Multi-label classifications exist in many real world applications. This paper empirically studies the performance of a variety of multi-label classification algorithms. Some of them are developed based on problem transformation. Some of them are developed based on adaption. Our experimental results show that the adaptive Multi-Label K-Nearest Neighbor performs the best, followed by Random k-Label Set, followed by Classifier Chain and Binary Relevance. Adaboost.MH performs the worst, followed by Pruned Problem Transformation. Our experimental results also provide us the confidence of existing correlations among multi-labels. These insights shed light for future research directions on multi-label classifications.
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Tawiah, C.A., Sheng, V.S. (2013). A Study on Multi-label Classification. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2013. Lecture Notes in Computer Science(), vol 7987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39736-3_11
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DOI: https://doi.org/10.1007/978-3-642-39736-3_11
Publisher Name: Springer, Berlin, Heidelberg
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