Automated text content identification for document processing using a kernel-based support Vector Selection approach

Author

Benveniste, Steven M. ; Fargues, Monique P.

Author_Institution

ECE Dept., Naval Postgrad. Sch., Monterey, CA, USA

fYear

2009

fDate

1-4 Nov. 2009

Firstpage

366

Lastpage

370

Abstract

Automated text analysis and mining tools designed to identify the main topics of texts, chat room discussions, and web postings are an increasingly active research area due to the rapid explosion of Web information. This paper applies the nonlinear kernel-based Feature Vector Selection (FVS) approach followed by a Linear Discriminant Analysis (LDA) step to categorize unstructured text documents. Results are compared to those obtained using the Latent Semantic Analysis (LSA) approach commonly used in text categorization applications. Overall results, taking into account classification performances and computational load issues, show that the FVS-LDA implemented with a polynomial kernel of degree 1 and an added constant of 1 to be the best classifier for the database considered.

Keywords

data mining; pattern classification; support vector machines; text analysis; FVS-LDA approach; document processing; kernel-based feature vector selection; kernel-based support vector selection; latent semantic analysis; polynomial kernel; text categorization; text content identification; text mining; Adaptive filters; Computational efficiency; Data mining; Databases; Explosions; Information retrieval; Kernel; Linear discriminant analysis; Machine learning; Text categorization; Data Mining; Feature Vector Selection (FVS); Kernel Based Schemes; Single Value Decomposition (SVD); Text Categorization; Text Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2009 Conference Record of the Forty-Third Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4244-5825-7

Type

conf

DOI

10.1109/ACSSC.2009.5469831

Filename

5469831