TY - GEN
T1 - Using soft similarity in multi-label classification for reuters-21578 corpus
AU - Trejo, Victor Carrera
AU - Sidorov, Grigori
AU - Ibarra, Marco Moreno
AU - Jiménez, Sabino Miranda
AU - Martínez, Rodrigo Cadena
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - In classification tasks one of the main problems is to choose which features provide best results, i.e., Construct a vector space model. In this paper, we show how to complement traditional vector space model with the concept of soft similarity. We use the combination of the traditional tf-idf model with latent Dirichlet allocation applied in multi-label classification. We considered multi-label files of the Reuters-21578 corpus as study case. The methodology is evaluated using the multi-label algorithm Rakel1. We used the traditional tf-idf model as the baseline. We present the F1 measures for both models for various feature sets, preprocessing techniques and vector sizes. The new model obtains better results than the base line model.
AB - In classification tasks one of the main problems is to choose which features provide best results, i.e., Construct a vector space model. In this paper, we show how to complement traditional vector space model with the concept of soft similarity. We use the combination of the traditional tf-idf model with latent Dirichlet allocation applied in multi-label classification. We considered multi-label files of the Reuters-21578 corpus as study case. The methodology is evaluated using the multi-label algorithm Rakel1. We used the traditional tf-idf model as the baseline. We present the F1 measures for both models for various feature sets, preprocessing techniques and vector sizes. The new model obtains better results than the base line model.
KW - Latent Dirichlet allocation
KW - Multi-labeling
KW - Reuters-21578
KW - Semantics
KW - Soft similarity
KW - Tf-idf
KW - Vector space model
UR - http://www.scopus.com/inward/record.url?scp=84951013732&partnerID=8YFLogxK
U2 - 10.1109/MICAI.2014.7
DO - 10.1109/MICAI.2014.7
M3 - Contribución a la conferencia
AN - SCOPUS:84951013732
T3 - Proceedings of Special Session 2014 13th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence, MICAI 2014
SP - 3
EP - 8
BT - Proceedings of Special Session 2014 13th Mexican International Conference on Artificial Intelligence
A2 - Gelbukh, Alexander
A2 - Galicia-Haro, Sofia N.
A2 - Castro Espinoza, Felix
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014
Y2 - 16 November 2014 through 22 November 2014
ER -