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