DocumentCode :
3426061
Title :
Practical fuzzy decision trees
Author :
Abu-halaweh, N.M. ; Harrison, Robert W.
Author_Institution :
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA
fYear :
2009
fDate :
March 30 2009-April 2 2009
Firstpage :
211
Lastpage :
216
Abstract :
Decision-tree algorithms are one of the most popular applications in machine learning. The ID3 algorithm is an efficient method for building decision trees that form the basis for many decision tree programs. Fuzzy ID3 is an extension of the existing ID3 algorithm; it integrates fuzzy set theory and ID3 to overcome the effects of spurious precision in the data, to treat uncertainties in the data and to reduce the decision tree sensitivity to small changes in attribute values. In this paper, we introduce a modified version of fuzzy ID3 algorithm that integrates information gain and classification ambiguity to select the test attribute. The modified algorithm achieves better accuracy than the original Fuzzy ID3 as well as crisp programs such C4.5 on a wide range of datasets. We also introduce a new machine learning software tool based on fuzzy decision trees.
Keywords :
decision trees; fuzzy set theory; learning (artificial intelligence); Fuzzy ED3; classification ambiguity; decision tree algorithms; fuzzy decision trees; fuzzy set theory; information gain; machine learning; Cancer; Classification tree analysis; Decision trees; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Machine learning; Machine learning algorithms; Testing; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
Type :
conf
DOI :
10.1109/CIDM.2009.4938651
Filename :
4938651
Link To Document :
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