An improvement approach for word tendency using decision tree

  • Authors:

  • El-Sayed Atlam;Elmarhomy Ghada;Masao Fuketa;Kazuhiro Morita;Jun-ichi Aoe

  • Affiliations:

  • Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, Japan;Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, Japan;Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, Japan;Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, Japan;Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima, Japan

  • Venue:
  • KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
  • Year:
  • 2005

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Abstract

In every text, words have various frequencies and keywords have strong relationship with the subjects of their texts. Word frequencies change due to time-series variation over given periods of time. An early method estimated stability classes that indicate word popularity due to time-series variation based on frequency changes in text data over given periods using a decision tree. The estimation precision of the decision tree decreases when there is scattering of data number among classes. This paper suggests a new way to use a Random Sampling Method and proposes a new Data Copying Method to improve the estimation precision of decision tree. By using this new Data Copying Method, F-measures have improved: Increasing Class 9%; Relatively Constant Class 9%; Decreasing Class 18%.