DocumentCode
3031233
Title
Investigating Learning Methods for Binary Data
Author
Visa, Sofia ; Ralescu, Anca ; Ionescu, Mircea
Author_Institution
Univ. of Cincinnati, Cincinnati
fYear
2007
fDate
24-27 June 2007
Firstpage
441
Lastpage
445
Abstract
Michie et al. show in [1] that decision trees perform better than twenty other classification algorithms in classifying binary data. In this paper we further investigate this hypothesis by comparing the decision trees with a fuzzy set-based classifier and the naive Bayes on real and artificial datasets.
Keywords
Bayes methods; data analysis; decision trees; fuzzy set theory; learning (artificial intelligence); pattern classification; binary data classification algorithm; binary data learning method; decision tree; fuzzy set-based classifier; naive Bayes method; Australia; Classification algorithms; Classification tree analysis; Decision trees; Fuzzy sets; Humans; Learning systems; Machine learning; Neural networks; Voting; Naive Bayes; binary data; classification; decision trees; fuzzy classifiers;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 2007. NAFIPS '07. Annual Meeting of the North American
Conference_Location
San Diego, CA
Print_ISBN
1-4244-1213-7
Electronic_ISBN
1-4244-1214-5
Type
conf
DOI
10.1109/NAFIPS.2007.383880
Filename
4271103
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