Paper Info
Title
Induction of descriptive fuzzy classifiers with the Logitboost algorithm
Abstract
Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or “Logitboost". The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to learn fuzzy rules in classification problems.In this work, Logitboost is applied to learn fuzzy rules in classification problems, and its results are compared with that of Adaboost and other fuzzy rule learning algorithms. Contradicting the expected results, it is shown that the basic extension of the backfitting algorithm to learn classification rules may produce worse results than Adaboost does. We suggest that this is caused by the stricter requirements that Logitboost demands to the weak learners, which are not fulfilled by fuzzy rules. Finally, it is proposed a prefitting based modification of the Logitboost algorithm that avoids this problem
Year
DOI
Venue
2006
10.1007/s00500-005-0011-0
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Keywords
Field
DocType
genetic fuzzy systems · descriptive fuzzy rules · fuzzy adaboost · fuzzy logitboost,basic extension,logitboost algorithm,logitboost demand,adaboost algorithm,descriptive fuzzy classifier,classification problem,backfitting algorithm,fuzzy rule,expected result,greedy backfitting,classification rule,additive model,logistic regression
Neuro-fuzzy,AdaBoost,Fuzzy classification,Computer science,Fuzzy set operations,Algorithm,Boosting (machine learning),Artificial intelligence,LogitBoost,Genetic fuzzy systems,Machine learning,Fuzzy rule
Journal
Volume
Issue
ISSN
10
9
1433-7479
Citations 
PageRank 
References 
23
1.27
13
Authors
2
Name
Order
Citations
PageRank
José Otero155224.66
Luciano Sánchez237726.34