Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function

© 2020 ACM. Neuro-Fuzzy is one of the techniques commonly used in classification as it has been proven satisfactory in predicting results. The neuro-fuzzy system is divided into 3 layers: the first layer is the fuzzification process, the special hidden layer is bipolar transform, and the output laye...

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Main Authors: Kamonwan Chaisornying, Narissara Eiamkanitchat
Format: Conference Proceeding
Published: 2020
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Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090875002&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70420
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-704202020-10-14T08:30:13Z Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function Kamonwan Chaisornying Narissara Eiamkanitchat Computer Science © 2020 ACM. Neuro-Fuzzy is one of the techniques commonly used in classification as it has been proven satisfactory in predicting results. The neuro-fuzzy system is divided into 3 layers: the first layer is the fuzzification process, the special hidden layer is bipolar transform, and the output layer is the learning process. This research proposes the alternatives to the initialization of membership function (MF) for the Neuro-fuzzy system that maintains the properties of direct calculation and rule-based for classification. In the fuzzification phase, define membership functions in accordance with the characteristics of the data using five summarized values such as min, 1st quartile (Q1), median, 3rd quartile (Q3) and max to determine the initial position of MF to convert input data to fuzzy sets or linguistics variable. The Gaussian is used as MF and transforms members into bipolar in a special hidden layer to be sent to the final part of the artificial neural network. The 12 data sets with various classes and features are used in the experiment and give satisfactory results. 2020-10-14T08:30:13Z 2020-10-14T08:30:13Z 2020-07-17 Conference Proceeding 2-s2.0-85090875002 10.1145/3411174.3411190 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090875002&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70420
institution Chiang Mai University
building Chiang Mai University Library
continent Asia
country Thailand
Thailand
content_provider Chiang Mai University Library
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Kamonwan Chaisornying
Narissara Eiamkanitchat
Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
description © 2020 ACM. Neuro-Fuzzy is one of the techniques commonly used in classification as it has been proven satisfactory in predicting results. The neuro-fuzzy system is divided into 3 layers: the first layer is the fuzzification process, the special hidden layer is bipolar transform, and the output layer is the learning process. This research proposes the alternatives to the initialization of membership function (MF) for the Neuro-fuzzy system that maintains the properties of direct calculation and rule-based for classification. In the fuzzification phase, define membership functions in accordance with the characteristics of the data using five summarized values such as min, 1st quartile (Q1), median, 3rd quartile (Q3) and max to determine the initial position of MF to convert input data to fuzzy sets or linguistics variable. The Gaussian is used as MF and transforms members into bipolar in a special hidden layer to be sent to the final part of the artificial neural network. The 12 data sets with various classes and features are used in the experiment and give satisfactory results.
format Conference Proceeding
author Kamonwan Chaisornying
Narissara Eiamkanitchat
author_facet Kamonwan Chaisornying
Narissara Eiamkanitchat
author_sort Kamonwan Chaisornying
title Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
title_short Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
title_full Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
title_fullStr Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
title_full_unstemmed Increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
title_sort increasing predictive accuracy of neuro-fuzzy using quartiles to initialize the membership function
publishDate 2020
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090875002&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/70420
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