RT Journal Article
T1 TCBR-HMM: An HMM-based text classifier with a CBR system
A1 Borrajo Diz, Maria Lourdes
A1 Seara Vieira, Adrián
A1 Lorenzo Iglesias, Eva Maria
K1 1203.17 Informática
AB This paper presents an innovative solution to model distributed adaptive systems in biomedical environments. We present an original TCBR-HMM (Text Case Based Reasoning-Hidden Markov Model) for biomedical text classification based on document content. The main goal is to propose a more effective classifier than current methods in this environment where the model needs to be adapted to new documents in an iterative learning frame. To demonstrate its achievement, we include a set of experiments, which have been performed on OSHUMED corpus. Our classifier is compared with Naive Bayes and SVM techniques, commonly used in text classification tasks. The results suggest that the TCBR-HMM Model is indeed more suitable for document classification. The model is empirically and statistically comparable to the SVM classifier and outperforms it in terms of time efficiency.
PB Applied Soft Computing
SN 15684946
YR 2015
FD 2015-01
LK http://hdl.handle.net/11093/6155
UL http://hdl.handle.net/11093/6155
LA eng
NO Applied Soft Computing, 26, 463-473 (2015)
NO Ministerio de Ciencia e Innovación | Ref. TIN2009-14057-C03-02
DS Investigo
RD 17-sep-2024