TY - GEN
T1 - Empirical comparison of Multi-Label classification algorithms
AU - Tawiah, Clifford A.
AU - Sheng, Victor S.
PY - 2013
Y1 - 2013
N2 - 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 the correlations among multilabels. These insights shed light for future research directions on multi-label classifications.
AB - 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 the correlations among multilabels. These insights shed light for future research directions on multi-label classifications.
UR - http://www.scopus.com/inward/record.url?scp=84891610061&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84891610061
SN - 9781577356158
T3 - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
SP - 1645
EP - 1646
BT - Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013
T2 - 27th AAAI Conference on Artificial Intelligence, AAAI 2013
Y2 - 14 July 2013 through 18 July 2013
ER -