Proceedings ArticleDOI
Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems
M. Umanol,H. Okamoto,I. Hatono,H. Tamura,F. Kawachi,S. Umedzu,J. Kinoshita +6 more
- pp 2113-2118
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TLDR
A new version of ID3 algorithm is proposed to generate an understandable fuzzy decision tree using fuzzy sets defined by a user to be applied to diagnosis for potential transformers by analyzing gas in oil.Abstract:
A popular and particularly efficient method for making a decision tree for classification from symbolic data is ID3 algorithm. Revised algorithms for numerical data have been proposed, some of which divide a numerical range into several intervals or fuzzy intervals. Their decision trees, however, are not easy to understand. We propose a new version of ID3 algorithm to generate an understandable fuzzy decision tree using fuzzy sets defined by a user. We apply it to diagnosis for potential transformers by analyzing gas in oil. >read more
Citations
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Journal ArticleDOI
A complete fuzzy decision tree technique
Cristina Olaru,Louis Wehenkel +1 more
TL;DR: This method combines tree growing and pruning, to determine the structure of the soft decision tree, with refitting and backfitting, to improve its generalization capabilities.
Journal ArticleDOI
Decision Trees for Uncertain Data
TL;DR: This work discovers that the accuracy of a decision tree classifier can be much improved if the "complete information" of a data item (taking into account the probability density function (pdf)) is utilized.
Journal ArticleDOI
Induction of multiple fuzzy decision trees based on rough set technique
TL;DR: A numerical experiment on real data indicates that the proposed multiple tree induction is superior to the single tree induction based on the individual reduct or on the entire feature set for learning problems with many attributes.
Proceedings ArticleDOI
Decision Trees for Uncertain Data
TL;DR: This work discovers that the accuracy of a decision tree classifier can be much improved if the "complete information" of a data item (taking into account the probability density function (pdf)) is utilized.
Journal ArticleDOI
Belief decision trees: Theoretical foundations
TL;DR: This paper is concerned with the construction of the belief decision tree from a training set where the knowledge about the instances' classes is represented by belief functions, and its use for the classification of new instances where theknowledge about the attributes' values is representedby belief functions.
References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.