NDLI: A learning fuzzy decision tree and its application to tactile image

Content Provider	IEEE Xplore Digital Library
Author	Han-Pang Huang Chao-Chiun Liang
Copyright Year	1998
Description	Author affiliation: Dept. of Mech. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Han-Pang Huang)
Abstract	Decision trees play important roles in many fields such as pattern recognition and classification It is because they have simple, apparent and fast reasoning process. This paper develops an algorithm to generate a learning fuzzy decision tree. This algorithm firstly collects enough training data for generating a practical decision tree. It then uses fuzzy statistics to calculate fuzzy sets for representing the training data in order to save computing memory and increase generation speed. Finally, this algorithm uses a suboptimal criterion to learn a decision tree from the resultant fuzzy sets. The algorithm is applied to a general-purpose tactile force sensing system. This system uses fuzzy logic to interpolate the force data. Then, the proposed algorithm is used to generate the desired decision tree from the tactile data. Based on the decision tree, the objects can be online recognized precisely.
Starting Page	1578
Ending Page	1583
File Size	589382
Page Count	6
File Format	PDF
ISBN	0780344650
DOI	10.1109/IROS.1998.724823
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-10-17
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Training data Classification tree analysis Fuzzy sets Fuzzy logic Humans Artificial neural networks Statistical distributions Tactile sensors Chaos
Content Type	Text
Resource Type	Article

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