Paper Info
Title
Fuzziness and Performance: An Empirical Study with Linguistic Decision Trees
Abstract
Generally, there are two main streams of theories for studying uncertainties. One is probability theory and the other is fuzzy set theory. One of the basic ideas of fuzzy set theory is how to define and interpret membership functions. In this paper, we will study tree-structured data mining model based on a new interpretation of fuzzy theory. In this new theory, fuzzy labels will be used for modelling. The membership function is interpreted as appropriateness degrees for using labels to describe a fuzzy concept. Each fuzzy concept is modelled by a distribution on the appropriate fuzzy label sets. Previous work has shown that the new model outperforms some well-known data mining models such as Naive Bayes and Decision trees. However, the fuzzy labels used in previous works were predefined. We are interested in study the influences on the performance by using fuzzy labels with different degrees of overlapping. We test a series of UCI datasets and the results show that the performance of the model increased almost monotonically with the increase of the overlapping between fuzzy labels. For this empirical study with the LDT model, we can conclude that more fuzziness implies better performance.
Year
DOI
Venue
2007
10.1007/978-3-540-72950-1_40
IFSA (1)
Keywords
Field
DocType
ldt model,empirical study,fuzzy set theory,linguistic decision trees,fuzzy theory,probability theory,new theory,fuzzy label,membership function,appropriate fuzzy label set,previous work,fuzzy concept,fuzzy set,naive bayes,data mining,decision tree,tree structure
Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy mathematics,Fuzzy set,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Membership function,Machine learning
Conference
Volume
ISSN
Citations 
4529
0302-9743
2
PageRank 
References 
Authors
0.37
5
2
Name
Order
Citations
PageRank
Zengchang Qin143945.46
Jonathan Lawry217219.06