Interval-Valued Fuzzy Decision Trees with Optimal Neighbourhood Perimeter
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.
The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link. ©2014 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Citation : Lertworaprachaya, Y., Yang, Y. and John, R. (2014) Interval-Valued Fuzzy Decision Trees with Optimal Neighbourhood Perimeter. Applied Soft Computing, 24, pp. 851-856
ISSN : 1568-4946
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes