Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/12629
Title: Improving the performance of classification models with fuzzy cognitive maps
Authors: Christodoulou, Panayiotis 
Christoforou, Andreas 
Andreou, Andreas S. 
Keywords: Classification models;Fuzzy Cognitive Maps;Prediction accuracy
Category: Computer and Information Sciences;Electrical Engineering - Electronic Engineering - Information Engineering
Field: Natural Sciences;Engineering and Technology
Issue Date: Jul-2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE International Conference on Fuzzy Systems, 2017, Naples, Italy, 9-12 July
DOI: https://doi.org/10.1109/FUZZ-IEEE.2017.8015422
Conference: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) 
Abstract: This paper presents a novel approach to improve the accuracy of classification models used for prediction purposes by integrating a Fuzzy Cognitive Map (FCM) to produce a hybrid model. The proposed methodology first uses the FCM to discover latent correlations that exist between the data in order to form a single variable. This variable is then fed in the classification model as part of the training and testing phases to enhance its accuracy. Experimental results using datasets describing two different problems suggested noteworthy improvements in the accuracy of various classification models.
ISBN: 978-150906034-4
ISSN: 1558-4739
DOI: 10.1109/FUZZ-IEEE.2017.8015422
Rights: © 2017 IEEE.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

Show full item record

Page view(s) 50

60
Last Week
1
Last month
5
checked on Aug 18, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.