Skip to main content
Article thumbnail
Location of Repository

Examining Variations of Prominent Features in Genre Classification.

By Dr Yunhyong Kim and Seamus Ross

Abstract

This paper investigates the correlation between features of three types (visual, stylistic and topical types) and genre classes. The majority of previous studies in automated genre classification have created models based on an amalgamated representation of a document using a combination of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. In this paper we use classifiers independently modeled on three groups of features to examine six genre classes to show that the strongest features for making one classification is not necessarily the best features for carrying out another classification.

Topics: V Tools, M Resource Discovery, LA Ingest, LB Management, EA Metadata
Year: 2007
DOI identifier: 10.1109/hicss.2008.157
OAI identifier: oai:eprints.erpanet.org:136
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://eprints.erpanet.org/136... (external link)
  • Suggested articles

    Citations

    1. (1998). A Tutorial on support vector machines Discovery, doi
    2. (2006). a ZerotoMultiGenre Classification textes pour
    3. (2006). a ZerotoMultiGenre Classification pour
    4. (1997). Automatic
    5. (1997). Automatic text
    6. Automatic categorization of email into folders: benchmark experiments
    7. Chen, beliefs based on confusion matrix for combining multiple classifiers. doi
    8. Clustering document images using a bag of symbols representation. doi
    9. documents Information doi
    10. Finegrained document doi
    11. (2006). Genre classification in automated doi
    12. (2007). Genre Hybridism and Individualization, doi
    13. Kim, Automated
    14. (2001). Random forests. doi
    15. (2005). the web: genre classification doi
    16. Witten, machine learning tools and techniques. 2nd Edition, doi

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.