TY - GEN
T1 - Fuzzy clustering for semi-supervised learning - Case study
T2 - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012
AU - Poria, Soujanya
AU - Gelbukh, Alexander
AU - Das, Dipankar
AU - Bandyopadhyay, Sivaji
PY - 2013
Y1 - 2013
N2 - We consider the task of semi-supervised classification: extending category labels from a small dataset of labeled examples to a much larger set. We show that, at least on our case study task, unsupervised fuzzy clustering of the unlabeled examples helps in obtaining the hard clusters. Namely, we used the membership values obtained with fuzzy clustering as additional features for hard clustering. We also used these membership values to reduce the confusion set for the hard clustering. As a case study, we use applied the proposed method to the task of constructing a large emotion lexicon by extending the emotion labels from the WordNet Affect lexicon using various features of words. Some of the features were extracted from the emotional statements of the freely available ISEAR dataset; other features were WordNet distance and the similarity measured via the polarity scores in the SenticNet resource. The proposed method classified words by emotion labels with high accuracy.
AB - We consider the task of semi-supervised classification: extending category labels from a small dataset of labeled examples to a much larger set. We show that, at least on our case study task, unsupervised fuzzy clustering of the unlabeled examples helps in obtaining the hard clusters. Namely, we used the membership values obtained with fuzzy clustering as additional features for hard clustering. We also used these membership values to reduce the confusion set for the hard clustering. As a case study, we use applied the proposed method to the task of constructing a large emotion lexicon by extending the emotion labels from the WordNet Affect lexicon using various features of words. Some of the features were extracted from the emotional statements of the freely available ISEAR dataset; other features were WordNet distance and the similarity measured via the polarity scores in the SenticNet resource. The proposed method classified words by emotion labels with high accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84875844919&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37807-2_7
DO - 10.1007/978-3-642-37807-2_7
M3 - Contribución a la conferencia
SN - 9783642378065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 73
EP - 86
BT - Advances in Artificial Intelligence - 11th Mexican International Conference on Artificial Intelligence, MICAI 2012, Revised Selected Papers
Y2 - 27 October 2012 through 4 November 2012
ER -