Sciweavers

Share
ISDA
2008
IEEE

Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation

8 years 9 months ago
Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation
In multi-label text databases one or more labels, or categories, can be assigned to a single document. In many such databases there can be correlation on the assignment of subsets of the set of categories. This can be exploited to improve machine learning techniques devoted to multilabel text categorization. In this paper, we examine a Virtual Generalizing Random Access Memory Weightless Neural Network (VG-RAM WNN for short) architecture that takes advantage of the correlation between categories to improve text-categorization performance. We compare the performance of this architecture, that we named Data Correlated VG-RAM WNN (VG-RAM WNN-COR), with that of standard VG-RAM WNN using four multi-label categorization performance metrics: one-error, ranking loss, average precision and hamming loss. Our experimental results show that VG-RAM WNN-COR has an overall better performance than VG-RAM WNN for the set of metrics considered.
Alberto Ferreira de Souza, Claudine Badue, Bruno Z
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where ISDA
Authors Alberto Ferreira de Souza, Claudine Badue, Bruno Zanetti Melotti, Felipe Pedroni, Fernando Libio L. Almeida
Comments (0)
books