@inproceedings{c8417d7fe7d146038d9cc753916d9060,
title = "A study on multi-label classification",
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.",
keywords = "Adaboost.MH, Binary Relevance, Classifier Chain, Multi-Label K-Nearest Neighbor, Pruned Problem Transformation, Random k-Label Set, multi-label classification",
author = "Tawiah, {Clifford A.} and Sheng, {Victor S.}",
year = "2013",
doi = "10.1007/978-3-642-39736-3_11",
language = "English",
isbn = "9783642397356",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "137--150",
booktitle = "Advances in Data Mining",
note = "13th Industrial Conference on Advances in Data Mining, ICDM 2013 ; Conference date: 16-07-2013 Through 21-07-2013",
}