TY - GEN
T1 - Adaptive classifier selection in large-scale hierarchical classification
AU - Partalas, Ioannis
AU - Babbar, Rohit
AU - Gaussier, Eric
AU - Amblard, Cecile
PY - 2012/11/12
Y1 - 2012/11/12
N2 - Going beyond the traditional text classification, involving a few tens of classes, there has been a surge of interest in automatic document categorization in large taxonomies where the number of classes range from hundreds of thousands to millions. Due to the complex nature of the learning problem posed in such scenarios, one needs to adapt the conventional classification schemes to suit this domain. This paper presents a novel approach for classifier selection in large hierarchies, which is based on exploiting training data heterogeneity across the hierarchy. We also present a meta-learning framework for further flexibility in classifier selection. The experimental results demonstrate the applicability of our approach, which achieves accuracy comparable to the state-of-the-art and is also significantly faster for prediction.
AB - Going beyond the traditional text classification, involving a few tens of classes, there has been a surge of interest in automatic document categorization in large taxonomies where the number of classes range from hundreds of thousands to millions. Due to the complex nature of the learning problem posed in such scenarios, one needs to adapt the conventional classification schemes to suit this domain. This paper presents a novel approach for classifier selection in large hierarchies, which is based on exploiting training data heterogeneity across the hierarchy. We also present a meta-learning framework for further flexibility in classifier selection. The experimental results demonstrate the applicability of our approach, which achieves accuracy comparable to the state-of-the-art and is also significantly faster for prediction.
KW - Classifier Selection
KW - Hierarchical Classification
KW - Meta- learning
UR - http://www.scopus.com/inward/record.url?scp=84869017598&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34487-9_74
DO - 10.1007/978-3-642-34487-9_74
M3 - Chapter in a published conference proceeding
AN - SCOPUS:84869017598
SN - 9783642344862
VL - 7665
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 612
EP - 619
BT - Neural Information Processing - 19th International Conference, ICONIP 2012, Proceedings
T2 - 19th International Conference on Neural Information Processing, ICONIP 2012
Y2 - 12 November 2012 through 15 November 2012
ER -