Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/12674
Title: A hybrid prediction model integrating Fuzzy Cognitive Maps with Support Vector Machines
Authors: Christodoulou, Panayiotis 
Christoforou, Andreas 
Andreou, Andreas S. 
Keywords: Classification Tree;Fuzzy Cognitive Maps;Linear Discrimination;Machine learning;Prediction;Support Vector Machine;Weighted k-NN
Category: Computer and Information Sciences;Electrical Engineering - Electronic Engineering - Information Engineering
Field: Natural Sciences;Engineering and Technology
Issue Date: 26-Apr-2017
Publisher: SciTePress
Source: 19th International Conference on Enterprise Information Systems, 2017, vol. 1, pp. 554-564, 26-29 April
Link: http://www.scitepress.org/Papers/2017/63294/63294.pdf
Conference: International Conference on Enterprise Information Systems 
Abstract: This paper introduces a new hybrid prediction model combining Fuzzy Cognitive Maps (FCM) and Support Vector Machines (SVM) to increase accuracy. The proposed model first uses the FCM part to discover correlation patterns and interrelationships that exist between data variables and form a single latent variable. It then feeds this variable to the SVM part to improve prediction capabilities. The efficacy of the hybrid model is demonstrated through its application on two different problem domains. The experimental results show that the proposed model is better than the traditional SVM model and also outperforms other widely used supervised machine-learning techniques like Weighted k-NN, Linear Discrimination Analysis and Classification Trees.
ISBN: 978-989758247-9
DOI: 10.5220/0006329405540564
Rights: © 2017 SCITEPRESS
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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