Title :
Investigating Learning Methods for Binary Data
Author :
Visa, Sofia ; Ralescu, Anca ; Ionescu, Mircea
Author_Institution :
Univ. of Cincinnati, Cincinnati
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;
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
DOI :
10.1109/NAFIPS.2007.383880
