Book chapter
Improving Performance of Decision Trees for Recommendation Systems by Features Grouping Method
Recent Advances in Information and Communication Technology, Vol.265, pp.223-232
Springer International Publishing
2014
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.
Details
- Title
- Improving Performance of Decision Trees for Recommendation Systems by Features Grouping Method
- Authors/Creators
- S. Wanapu (Author/Creator)C.C. Fung (Author/Creator)J. Kajornrit (Author/Creator)S. Niwattanakula (Author/Creator)N. Chamnongsria (Author/Creator)
- Contributors
- S. Boonkrong (Editor)H. Unger (Editor)P. Meesad (Editor)
- Publication Details
- Recent Advances in Information and Communication Technology, Vol.265, pp.223-232
- Publisher
- Springer International Publishing; Switzerland
- Identifiers
- 991005544531507891
- Copyright
- 2014 Springer International Publishing Switzerland
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Book chapter
- Note
- Series Title: Advances in Intelligent Systems and Computing; Vol. 265
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