Clustering unlabeled data with SOMs improves classification of labeled real-world data

Author

Dara, Rozita ; Kremer, Stefan C. ; Stacey, Deborah A.

Author_Institution

Dept. of Comput. & Inf. Sci., Guelph Univ., Ont., Canada

Volume

3

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

2237

Lastpage

2242

Abstract

We show the use of a self organizing map to cluster unlabeled data and to infer possible labelings from the clusters. Our inferred labels are presented to a multilayer perceptron along with labeled data, performance is improved over using only the labeled data. Results are presented for a number of popular real-world benchmark problems from domains other than text. This shows one way in which unlabeled data can be used to enhance supervised learning in a general-purpose neural network

Keywords

learning (artificial intelligence); multilayer perceptrons; pattern classification; pattern clustering; self-organising feature maps; SOMs; classification; general-purpose neural network; labeled real-world data; multilayer perceptron; self organizing map; supervised learning; unlabeled data; Computational Intelligence Society; Costs; Humans; Information science; Labeling; Neural networks; Organizing; Supervised learning; System testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007489

Filename

1007489