DocumentCode :
160437
Title :
Modified fuzzy hyperline-segment neural network for classification with mixed attribues
Author :
Shinde, S.V. ; Kulkarni, U.V.
Author_Institution :
Dept. Inf. Technol., Pimpri Chinchwad Coll. of Eng., Pune, India
fYear :
2014
fDate :
11-13 July 2014
Firstpage :
1
Lastpage :
7
Abstract :
The fuzzy hyperline segment neural network (FHLSNN) utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperline segments. The fuzzy set hyperline segment is a n-dimensional hyperline segment defined by two end points with a corresponding membership function. In FHLSNN, membership function calculates membership value of the input pattern based on its distance from both the end points of the hyperline segment. But sometimes input pattern is nearer to the hyperline segment but far from its endpoints. To solve this problem, this paper proposes modified fuzzy hyperline segment neural network (MFHLSNN). In MHLSNN membership function is based on minimum of the distance of the input pattern from the midpoint of the hyperline segment and its distance from both the end points. The proposed model is applied to eight different benchmark datasets taken from the UCI machine learning repository. The experimental results of the MFHLSNN are compared with earlier methods like fuzzy min-max neural network, generalized fuzzy min-max neural network and fuzzy hyperline segment neural network. These results show that the MFHLSNN gives improved performance as compared to its earlier methods.
Keywords :
fuzzy set theory; learning (artificial intelligence); minimax techniques; neural nets; MFHLSNN; UCI machine learning repository; fuzzy set hyperline segments; generalized fuzzy min-max neural network; membership function; modified fuzzy hyperline-segment neural network; Accuracy; Equations; Iris; Iris recognition; Mathematical model; Neural networks; Training; Fuzzy min-max neural network; classification; continuous attributes; discrete attributes; fuzzy hyperline-segment neural network; rule extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Communication and Networking Technologies (ICCCNT), 2014 International Conference on
Conference_Location :
Hefei
Print_ISBN :
978-1-4799-2695-4
Type :
conf
DOI :
10.1109/ICCCNT.2014.6963078
Filename :
6963078
Link To Document :
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