Interval-valued fuzzy decision trees with optimal neighbourhood perimeter

Lertworaprachaya, Youdthachai; Yang, Yingjie; John, Robert

Abstract

This research proposes a new model for constructing decision trees using interval-valued fuzzy membership values. Most existing fuzzy decision trees do not consider the uncertainty associated with their membership values, however, precise values of fuzzy membership values are not always possible. In this paper, we represent fuzzy membership values as intervals to model uncertainty and employ the look-ahead based fuzzy decision tree induction method to construct decision trees. We also investigate the significance of different neighbourhood values and define a new parameter insensitive to specific data sets using fuzzy sets. Some examples are provided to demonstrate the effectiveness of the approach.

Citation

Lertworaprachaya, Y., Yang, Y., & John, R. (2014). Interval-valued fuzzy decision trees with optimal neighbourhood perimeter. Applied Soft Computing, 24, https://doi.org/10.1016/j.asoc.2014.08.060

• Journal Article Type: Article
• Acceptance Date: Aug 14, 2014
• Online Publication Date: Sep 4, 2014
• Publication Date: Nov 1, 2014
• Deposit Date: Sep 27, 2014
• Publicly Available Date: Sep 27, 2014
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 24
• DOI: https://doi.org/10.1016/j.asoc.2014.08.060
• Keywords: Look-ahead based fuzzy decision tree induction; Optimal perimeter; Interval-valued fuzzy decision trees
• Public URL: https://nottingham-repository.worktribe.com/output/993960
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1568494614004256
• Contract Date: Sep 27, 2014