TY - GEN
T1 - Classifier chains for multi-label classification
AU - Read, Jesse
AU - Pfahringer, Bernhard
AU - Holmes, Geoff
AU - Frank, Eibe
PY - 2009/1/1
Y1 - 2009/1/1
N2 - The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
AB - The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
U2 - 10.1007/978-3-642-04174-7_17
DO - 10.1007/978-3-642-04174-7_17
M3 - Conference contribution
AN - SCOPUS:70349968175
SN - 3642041736
SN - 9783642041730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 269
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings
PB - Springer Verlag
T2 - 9th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009
Y2 - 7 September 2009 through 11 September 2009
ER -