DocumentCode :
2651857
Title :
Classification by Clusters Analysis - An Ensemble Technique in a Semi-supervised Classification
Author :
Jurek, Anna ; Bi, Yaxin ; Wu, Shengli ; Nugent, Chris
Author_Institution :
Sch. of Comput. & Math., Univ. of Ulster, Newtownabbey, UK
fYear :
2011
fDate :
7-9 Nov. 2011
Firstpage :
876
Lastpage :
878
Abstract :
In this work we adopt a previously introduced meta-learning classification method for semi-supervised learning problems. In our previous work we illustrated that the method is successful when applied in a supervised classification problem. In our current work the results demonstrate that following refinements made to the method it can be successfully applied to semi-supervised classification cases.
Keywords :
learning (artificial intelligence); pattern classification; clusters analysis; ensemble technique; meta-learning classification method; semi-supervised classification; semi-supervised learning problems; supervised classification problem; Accuracy; Bismuth; Euclidean distance; Learning systems; Stacking; Training; Training data; classifier ensemble; clustering; combining classifiers; meta learning; semi-supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
ISSN :
1082-3409
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
Type :
conf
DOI :
10.1109/ICTAI.2011.137
Filename :
6103428
Link To Document :
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