Abstract
Decision bees have been widely and successfully used in machine learning. However, they have suffered from overfitting in noisy domains. This problem has been remedied, in C4.5 for example, by tree pruning, resulting in improved performance. More recently, fuzzy representations have been combined with decision trees. How does the performance of fuzzy decision trees compare to that of pruned decision trees? The authors propose a comparative study of pruned decision trees and fuzzy decision trees. Further, for continuous inputs, they explore different ways: (1) for selecting the granularity of the fuzzy input variables; and (2) for defining the membership functions of the fuzzy input values. We carry out an empirical study using 12 data sets. The results show that a fuzzy decision tree constructed using FID3, combined with fuzzy clustering (to build membership functions) and cluster validity (to decide on granularity), is superior to pruned decision trees.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
