Improving automatic text classification by integrated feature analysis

Lazaro S.P. Busagala, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.

Original languageEnglish
Pages (from-to)1101-1109
Number of pages9
JournalIEICE Transactions on Information and Systems
VolumeE91-D
Issue number4
DOIs
Publication statusPublished - Apr 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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