TY - GEN
T1 - Text representation in multi-label classification
T2 - 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2011
AU - Alfaro, Rodrigo
AU - Allende, Héctor
PY - 2011
Y1 - 2011
N2 - Automatic text classification is the task of assigning unseen documents to a predefined set of classes. Text representation for classification purposes has been traditionally approached using a vector space model due to its simplicity and good performance. On the other hand, multi-label automatic text classification has been typically addressed either by transforming the problem under study to apply binary techniques or by adapting binary algorithms to work with multiple labels. In this paper we present two new representations for text documents based on label-dependent term-weighting for multi-label classification. We focus on modifying the input. Performance was tested with a well-known dataset and compared to alternative techniques. Experimental results based on Hamming loss analysis show an improvement against alternative approaches.
AB - Automatic text classification is the task of assigning unseen documents to a predefined set of classes. Text representation for classification purposes has been traditionally approached using a vector space model due to its simplicity and good performance. On the other hand, multi-label automatic text classification has been typically addressed either by transforming the problem under study to apply binary techniques or by adapting binary algorithms to work with multiple labels. In this paper we present two new representations for text documents based on label-dependent term-weighting for multi-label classification. We focus on modifying the input. Performance was tested with a well-known dataset and compared to alternative techniques. Experimental results based on Hamming loss analysis show an improvement against alternative approaches.
KW - Multi-label text classification
KW - problem transformation
KW - text modelling
UR - http://www.scopus.com/inward/record.url?scp=79955123970&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-20267-4_7
DO - 10.1007/978-3-642-20267-4_7
M3 - Conference contribution
AN - SCOPUS:79955123970
SN - 9783642202667
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 70
BT - Adaptive and Natural Computing Algorithms - 10th International Conference, ICANNGA 2011, Proceedings
Y2 - 14 April 2011 through 16 April 2011
ER -