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:130

Suggested articles

Citations

  1. (1998). A Tutorial on support vector machines Discovery, doi
  2. (2006). a ZerotoMultiGenre Classification pour
  3. (1997). Automatic
  4. (1997). Automatic text
  5. Automatic categorization of email into folders: benchmark experiments
  6. Chen, beliefs based on confusion matrix for combining multiple classifiers. doi
  7. Clustering document images using a bag of symbols representation. doi
  8. documents Information doi
  9. Finegrained document doi
  10. Kim, Automated
  11. (2001). Random forests. doi
  12. (2005). the web: genre classification doi

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