Information fusion for text classification — an experimental comparison

Autor:	Venu Dasigi, Reinhold C. Mann, Vladimir Protopopescu
Rok vydání:	2001
Předmět:	Information fusion Information retrieval Artificial neural network Artificial Intelligence Computer science Feature (computer vision) Signal Processing Computer Vision and Pattern Recognition Semantics Software Latent semantic indexing
Zdroj:	Pattern Recognition. 34:2413-2425
ISSN:	0031-3203
DOI:	10.1016/s0031-3203(00)00171-0
Popis:	This article reports on our experiments and results on the effectiveness of different feature sets and information fusion from some combinations of them in classifying free text documents into a given number of categories. We use different feature sets and integrate neural network learning into the method. The feature sets are based on the "latent semantics" of a reference library - a collection of documents adequately representing the desired concepts. We found that a larger reference library is not necessarily better. Information fusion almost always gives better results than the individual constituent feature sets, with certain combinations doing better than the others.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f0d0e21b77376eebfcc7cca4d2b1f4d5 https://doi.org/10.1016/s0031-3203(00)00171-0 Zobrazit plný text záznamu Full Text from ScienceDirect