NDLI: An Empirical Comparison of Two Methods for Fuzzy Density

Content Provider	IEEE Xplore Digital Library
Author	Zhizhou Kong Zixing Cai
Copyright Year	2009
Abstract	Information Fusion is a valid way which can decrease the uncertainty of making decision, and is also a hotspot. The paper makes some work on a important problem about Fuzzy Integral, that is how to get the Fuzzy Density, and compares two typical means. Based on 11 UCI data set, this paper conducts the compared experiment of several Information Fusion methods. It is compared with references 4 and 5. The result shows that the Fuzzy Integral method based on probability is better than the Fuzzy Integral method based on beliefs, is also better than the best results of single classifiers in references 4. The result also shows that the Fuzzy Integral method based on beliefs is nearly equal to the best results of fusion classifiers in references 5 in general, better than the average fusion method, and is also better the best results of single classifiers in references 4.
Starting Page	433
Ending Page	436
File Size	346606
Page Count	4
File Format	PDF
ISBN	9780769538594
DOI	10.1109/IITA.2009.355
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Target tracking Fuzzy Integral Data engineering Educational institutions Electronic mail Information technology Compared Analysis Fuzzy sets Information science Computer aided instruction Fuzzy Density Signal processing
Content Type	Text
Resource Type	Article

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