Improving Performance of Decision Trees for Recommendation Systems by Features Grouping Method

  • Supachanun Wanapu
  • Chun Che Fung
  • Jesada Kajornrit
  • Suphakit Niwattanakula
  • Nisachol Chamnongsria
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)

Abstract

Recently, recommendation systems have become an important tool to support and improve decision making for educational purposes. However, developing recommendation systems is far from trivial and there are specific issues associated with individual problems. Low-correlated input features is a problem that influences the overall accuracy of decision tree models. Weak relationship between input features can cause decision trees work inefficiently. This paper reports the use of features grouping method to improve the classification accuracy of decision trees. Such method groups related input features together based on their ontologies. The new inherited features are then used instead as new features to the decision trees. The proposed method was tested with five decision tree models. The dataset used in this study were collected from schools in Nakhonratchasima province, Thailand. The experimental results indicated that the proposed method can improve the classification accuracy of all decision tree models. Furthermore, such method can significantly decrease the computational time in the training period.

Keywords

Recommendation Systems Decision Trees Features Grouping Learning Object Ontologies 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Supachanun Wanapu
    • 1
  • Chun Che Fung
    • 2
  • Jesada Kajornrit
    • 2
  • Suphakit Niwattanakula
    • 1
  • Nisachol Chamnongsria
    • 1
  1. 1.School of Information TechnologySuranaree University of TechnologyNakhonratchasimaThailand
  2. 2.School of Engineering and Information TechnologyMurdoch UniversityMurdochAustralia

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